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\n\n \n \n 2024\n \n \n (12)\n \n \n
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\n \n \n\n \n \n \n \n \n Human-in-the-Loop Informed Deep Learning Rectal Tumor Segmentation on Pre-Treatment MRI.\n \n \n \n\n\n \n Kong, M.; DeSilvio, T.; Bao, L.; Flannery, B.; Parker, B.; Tang, S.; Labbad, M.; O’Connor, G.; Gupta, A.; Steinhagen, E.; Purysko, A.; Hall, W.; Liska, D.; Marderstein, E.; Carroll, A.; Crittenden, M.; Gough, M.; Young, K.; and Viswanath, S.\n\n\n \n\n\n\n 2024.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@misc{Kong2024,\n abstract = {Precise segmentation of rectal cancer tumors on routine MRI is critical for accurate clinical staging and downstream computational analyses. While deep learning-based segmentation algorithms have shown much promise in automating the otherwise tedious, subjective, and costly process of manual segmentation, they require significant amounts of manually annotated data for training. To address these limitations of deep learning-based segmentation models, we present a novel deep learning framework that incorporates human-in-the-loop (HITL) refinement to automatically delineate rectal tumors on multi-plane pre-treatment MR imaging. When evaluated on multiple holdout validation cohorts including a clinical trial dataset, the post-HITL segmentation model significantly outperformed the pre-HITL model with median dice similarity coefficient of 0.763 and Hausdorff distance of 28.4mm in comparison to 0.601 and 31.8mm, respectively. HITL refinement learning also significantly accelerated the manual annotation process by 20 minutes. HITL learning represents a feasible, effective, and efficient solution to semi-automated tumor segmentation on routine rectal cancer MRI scans.},\n author = {M. Kong and T. DeSilvio and L. Bao and B.T. Flannery and B.N. Parker and S. Tang and M. Labbad and G. O’Connor and A. Gupta and E.F. Steinhagen and A.S. Purysko and W.A. Hall and D. Liska and E.L. Marderstein and A. Carroll and M.R. Crittenden and M.J. Gough and K.H. Young and S.E. Viswanath},\n doi = {10.1117/12.3008637},\n isbn = {9781510671584},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {MRI,Rectal cancer,deep learning,human-in-the-loop,multi-plane,segmentation},\n title = {Human-in-the-Loop Informed Deep Learning Rectal Tumor Segmentation on Pre-Treatment MRI},\n volume = {12927},\n year = {2024},\n}\n\n\n Precise segmentation of rectal cancer tumors on routine MRI is critical for accurate clinical staging and downstream computational analyses. While deep learning-based segmentation algorithms have shown much promise in automating the otherwise tedious, subjective, and costly process of manual segmentation, they require significant amounts of manually annotated data for training. To address these limitations of deep learning-based segmentation models, we present a novel deep learning framework that incorporates human-in-the-loop (HITL) refinement to automatically delineate rectal tumors on multi-plane pre-treatment MR imaging. When evaluated on multiple holdout validation cohorts including a clinical trial dataset, the post-HITL segmentation model significantly outperformed the pre-HITL model with median dice similarity coefficient of 0.763 and Hausdorff distance of 28.4mm in comparison to 0.601 and 31.8mm, respectively. HITL refinement learning also significantly accelerated the manual annotation process by 20 minutes. HITL learning represents a feasible, effective, and efficient solution to semi-automated tumor segmentation on routine rectal cancer MRI scans.\n
\n\n\n\n \n\n \n \n \n \n \n Spatial Attention Wavelon Network (SpAWN) for Survival-based Risk Stratification of Kidney Cancers via CT.\n \n \n \n\n\n \n Flannery, B.; DeSilvio, T.; Sadri, A.; Hariri, M.; Remer, E.; Nguyen, J.; and Viswanath, S.\n\n\n \n\n\n\n 2024.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@misc{Flannery2024,\n abstract = {Risk stratification of kidney cancers based on survival at diagnosis could enable more informed treatment decisions toward improved patient survival. Given the limited prognostic ability of clinical evaluation in segregating long-term vs short-term survivors, we sought to develop prognostic models which could exploit diagnostic CT scans for overall survival prediction in kidney cancers. This requires overcoming challenges related to model interpretability (such that the model best utilizes the most relevant locations within or around the kidney) as well as model generalizability (to ensure optimal model performance despite limited cohort sizes). In this work, we present the Spatial Attention Wavelon Network (SpAWN), which leverages a novel pre-training spatial attention operation to guide localization of convolutional responses together with wavelon activation functions to overcome known issues with vanishing/exploding gradients that occur in limited cohorts with class imbalance. SpAWN was evaluated for prognosticating survival on two large-scale publicly available cohorts of over 400 CT scans from kidney cancer patients, with comprehensive ablation studies to confirm the utility of attention maps as well as wavelon activation functions. A kidney exterior focused SpAWN model (with wavelon activations) demonstrated the best overall validation performance in segregating low- and high-risk patients in a hold-out cohort of N=223 CT scans (c-index=0.58, p=0.03), and was significantly improved compared to any alternative strategy. Integrating spatial attention with wavelon activations represents a novel interpretable and robust approach for prognosticating overall survival in kidney cancers via CT scans.},\n author = {B.T. Flannery and T. DeSilvio and A.R. Sadri and M. Hariri and E.M. Remer and J.K. Nguyen and S.E. Viswanath},\n doi = {10.1117/12.3008727},\n isbn = {9781510671584},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Deep learning,Kidney cancer,Spatial Attention,Survival Prediction,Wavelon network},\n title = {Spatial Attention Wavelon Network (SpAWN) for Survival-based Risk Stratification of Kidney Cancers via CT},\n volume = {12927},\n year = {2024},\n}\n\n\n Risk stratification of kidney cancers based on survival at diagnosis could enable more informed treatment decisions toward improved patient survival. Given the limited prognostic ability of clinical evaluation in segregating long-term vs short-term survivors, we sought to develop prognostic models which could exploit diagnostic CT scans for overall survival prediction in kidney cancers. This requires overcoming challenges related to model interpretability (such that the model best utilizes the most relevant locations within or around the kidney) as well as model generalizability (to ensure optimal model performance despite limited cohort sizes). In this work, we present the Spatial Attention Wavelon Network (SpAWN), which leverages a novel pre-training spatial attention operation to guide localization of convolutional responses together with wavelon activation functions to overcome known issues with vanishing/exploding gradients that occur in limited cohorts with class imbalance. SpAWN was evaluated for prognosticating survival on two large-scale publicly available cohorts of over 400 CT scans from kidney cancer patients, with comprehensive ablation studies to confirm the utility of attention maps as well as wavelon activation functions. A kidney exterior focused SpAWN model (with wavelon activations) demonstrated the best overall validation performance in segregating low- and high-risk patients in a hold-out cohort of N=223 CT scans (c-index=0.58, p=0.03), and was significantly improved compared to any alternative strategy. Integrating spatial attention with wavelon activations represents a novel interpretable and robust approach for prognosticating overall survival in kidney cancers via CT scans.\n
\n\n\n\n \n\n \n \n \n \n \n Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.\n \n \n \n\n\n \n Homsi, M. E.; Bane, O.; Fauveau, V.; Hectors, S.; Vietti-Violi, N.; Sylla, P.; Ko, H.; Cuevas, J.; Carbonell, G.; Nehlsen, A.; Vanguri, R.; Viswanath, S.; Jambawalikar, S.; Shaish, H.; and Taouli, B.\n\n\n \n\n\n\n Abdominal Radiology, 49. 2024.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{,\n abstract = {Purpose: To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). Methods: This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. Results: Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59–0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. Conclusion: Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results. Graphical abstract: (Figure presented.).},\n author = {M. El Homsi and O. Bane and V. Fauveau and S.J. Hectors and N. Vietti-Violi and P.A. Sylla and H. Ko and J.M. Cuevas and G. Carbonell and A.D. Nehlsen and R.S. Vanguri and S.E. Viswanath and S.R. Jambawalikar and H. Shaish and B.A. Taouli},\n doi = {10.1007/s00261-023-04128-0},\n issn = {23660058},\n issue = {3},\n journal = {Abdominal Radiology},\n keywords = {Diffusion,Magnetic resonance imaging,Radiomics,Rectal cancer},\n title = {Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics},\n volume = {49},\n year = {2024},\n}\n\n\n Purpose: To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). Methods: This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. Results: Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59–0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. Conclusion: Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results. Graphical abstract: (Figure presented.).\n
\n\n\n\n \n\n \n \n \n \n \n Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI.\n \n \n \n\n\n \n Zhao, W.; Hu, Z.; Kazerooni, A. F.; Koerzdoerfer, G.; Nittka, M.; Davatzikos, C.; Viswanath, S.; Wang, X.; Badve, C.; and Ma, D.\n\n\n \n\n\n\n Investigative Radiology, 59. 2024.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{Zhao2024,\n abstract = {Objective Given the limited repeatability and reproducibility of radiomic features derived from weighted magnetic resonance imaging (MRI), there may be significant advantages to using radiomics in conjunction with quantitative MRI. This study introduces a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction and evaluates its performance using quantitative MRI sequences including magnetic resonance fingerprinting (MRF) and apparent diffusion coefficient (ADC) mapping. Materials and Methods A multiscanner, scan-rescan dataset comprising whole-brain 3D quantitative (MRF T1, MRF T2, and ADC) and weighted MRI (T1w MPRAGE, T2w SPACE, and T2w FLAIR) from 5 healthy subjects was prospectively acquired. Subjects underwent 2 repeated acquisitions on 3 distinct 3 T scanners each, for a total of 6 scans per subject (30 total scans). First-order statistical (n = 23) and second-order texture (n = 74) radiomic features were extracted from 56 brain tissue regions of interest using the proposed PID method (for quantitative MRI) and conventional fixed bin number (FBN) discretization (for quantitative MRI and weighted MRI). Interscanner radiomic feature reproducibility was measured using the intraclass correlation coefficient (ICC), and the effect of image sequence (eg, MRF T1 vs T1w MPRAGE), as well as image discretization method (ie, PID vs FBN), on radiomic feature reproducibility was assessed using repeated measures analysis of variance. The robustness of PID and FBN discretization to segmentation error was evaluated by simulating segmentation differences in brainstem regions of interest. Radiomic features with ICCs greater than 0.75 following simulated segmentation were determined to be robust to segmentation. Results First-order features demonstrated higher reproducibility in quantitative MRI than weighted MRI sequences, with 30% (n = 7/23) features being more reproducible in MRF T1 and MRF T2 than weighted MRI. Gray level co-occurrence matrix (GLCM) texture features extracted from MRF T1 and MRF T2 were significantly more reproducible using PID compared with FBN discretization; for all quantitative MRI sequences, PID yielded the highest number of texture features with excellent reproducibility (ICC > 0.9). Comparing texture reproducibility of quantitative and weighted MRI, a greater proportion of MRF T1 (n = 225/370, 61%) and MRF T2 (n = 150/370, 41%) texture features had excellent reproducibility (ICC > 0.9) compared with T1w MPRAGE (n = 148/370, 40%), ADC (n = 115/370, 32%), T2w SPACE (n = 98/370, 27%), and FLAIR (n = 102/370, 28%). Physics-informed discretization was also more robust than FBN discretization to segmentation error, as 46% (n = 103/222, 46%) of texture features extracted from quantitative MRI using PID were robust to simulated 6 mm segmentation shift compared with 19% (n = 42/222, 19%) of weighted MRI texture features extracted using FBN discretization. Conclusions The proposed PID method yields radiomic features extracted from quantitative MRI sequences that are more reproducible and robust than radiomic features extracted from weighted MRI using conventional (FBN) discretization approaches. Quantitative MRI sequences also demonstrated greater scan-rescan robustness and first-order feature reproducibility than weighted MRI. },\n author = {W. Zhao and Z. Hu and A. Fathi Kazerooni and G. Koerzdoerfer and M. Nittka and C. Davatzikos and S.E. Viswanath and X. Wang and C.A. Badve and D. Ma},\n doi = {10.1097/RLI.0000000000001026},\n issn = {15360210},\n issue = {5},\n journal = {Investigative Radiology},\n keywords = {MRF,discretization,quantitative MRI,radiomics,reproducibility,robustness},\n title = {Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI},\n volume = {59},\n year = {2024},\n}\n\n\n Objective Given the limited repeatability and reproducibility of radiomic features derived from weighted magnetic resonance imaging (MRI), there may be significant advantages to using radiomics in conjunction with quantitative MRI. This study introduces a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction and evaluates its performance using quantitative MRI sequences including magnetic resonance fingerprinting (MRF) and apparent diffusion coefficient (ADC) mapping. Materials and Methods A multiscanner, scan-rescan dataset comprising whole-brain 3D quantitative (MRF T1, MRF T2, and ADC) and weighted MRI (T1w MPRAGE, T2w SPACE, and T2w FLAIR) from 5 healthy subjects was prospectively acquired. Subjects underwent 2 repeated acquisitions on 3 distinct 3 T scanners each, for a total of 6 scans per subject (30 total scans). First-order statistical (n = 23) and second-order texture (n = 74) radiomic features were extracted from 56 brain tissue regions of interest using the proposed PID method (for quantitative MRI) and conventional fixed bin number (FBN) discretization (for quantitative MRI and weighted MRI). Interscanner radiomic feature reproducibility was measured using the intraclass correlation coefficient (ICC), and the effect of image sequence (eg, MRF T1 vs T1w MPRAGE), as well as image discretization method (ie, PID vs FBN), on radiomic feature reproducibility was assessed using repeated measures analysis of variance. The robustness of PID and FBN discretization to segmentation error was evaluated by simulating segmentation differences in brainstem regions of interest. Radiomic features with ICCs greater than 0.75 following simulated segmentation were determined to be robust to segmentation. Results First-order features demonstrated higher reproducibility in quantitative MRI than weighted MRI sequences, with 30% (n = 7/23) features being more reproducible in MRF T1 and MRF T2 than weighted MRI. Gray level co-occurrence matrix (GLCM) texture features extracted from MRF T1 and MRF T2 were significantly more reproducible using PID compared with FBN discretization; for all quantitative MRI sequences, PID yielded the highest number of texture features with excellent reproducibility (ICC > 0.9). Comparing texture reproducibility of quantitative and weighted MRI, a greater proportion of MRF T1 (n = 225/370, 61%) and MRF T2 (n = 150/370, 41%) texture features had excellent reproducibility (ICC > 0.9) compared with T1w MPRAGE (n = 148/370, 40%), ADC (n = 115/370, 32%), T2w SPACE (n = 98/370, 27%), and FLAIR (n = 102/370, 28%). Physics-informed discretization was also more robust than FBN discretization to segmentation error, as 46% (n = 103/222, 46%) of texture features extracted from quantitative MRI using PID were robust to simulated 6 mm segmentation shift compared with 19% (n = 42/222, 19%) of weighted MRI texture features extracted using FBN discretization. Conclusions The proposed PID method yields radiomic features extracted from quantitative MRI sequences that are more reproducible and robust than radiomic features extracted from weighted MRI using conventional (FBN) discretization approaches. Quantitative MRI sequences also demonstrated greater scan-rescan robustness and first-order feature reproducibility than weighted MRI. \n
\n\n\n\n \n\n \n \n \n \n \n Quantification of Visceral Fat at the L5 Vertebral Body Level in Patients with Crohn’s Disease Using T2-Weighted MRI.\n \n \n \n\n\n \n Garuba, F.; Ganapathy, A.; McKinley, S.; Jani, K.; Lovato, A.; Viswanath, S.; McHenry, S.; Deepak, P.; and Ballard, D.\n\n\n \n\n\n\n Bioengineering, 11. 2024.\n \n\n\n\n
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\n\n\n@article{Garuba2024,\n abstract = {The umbilical or L3 vertebral body level is often used for body fat quantification using computed tomography. To explore the feasibility of using clinically acquired pelvic magnetic resonance imaging (MRI) for visceral fat measurement, we examined the correlation of visceral fat parameters at the umbilical and L5 vertebral body levels. We retrospectively analyzed T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) MR axial images from Crohn’s disease patients who underwent MRI enterography of the abdomen and pelvis over a three-year period. We determined the area/volume of subcutaneous and visceral fat from the umbilical and L5 levels and calculated the visceral fat ratio (VFR = visceral fat/subcutaneous fat) and visceral fat index (VFI = visceral fat/total fat). Statistical analyses involved correlation analysis between both levels, inter-rater analysis between two investigators, and inter-platform analysis between two image-analysis platforms. Correlational analysis of 32 patients yielded significant associations for VFI (r = 0.85; p < 0.0001) and VFR (r = 0.74; p < 0.0001). Intraclass coefficients for VFI and VFR were 0.846 and 0.875 (good agreement) between investigators and 0.831 and 0.728 (good and moderate agreement) between platforms. Our study suggests that the L5 level on clinically acquired pelvic MRIs may serve as a reference point for visceral fat quantification.},\n author = {F.O. Garuba and A.K. Ganapathy and S. McKinley and K.H. Jani and A. Lovato and S.E. Viswanath and S.A. McHenry and P. Deepak and D.H. Ballard},\n doi = {10.3390/bioengineering11060528},\n issn = {23065354},\n issue = {6},\n journal = {Bioengineering},\n keywords = {Crohn’s,L5,MRI,umbilicus,visceral fat},\n title = {Quantification of Visceral Fat at the L5 Vertebral Body Level in Patients with Crohn’s Disease Using T2-Weighted MRI},\n volume = {11},\n year = {2024},\n}\n\n\n The umbilical or L3 vertebral body level is often used for body fat quantification using computed tomography. To explore the feasibility of using clinically acquired pelvic magnetic resonance imaging (MRI) for visceral fat measurement, we examined the correlation of visceral fat parameters at the umbilical and L5 vertebral body levels. We retrospectively analyzed T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) MR axial images from Crohn’s disease patients who underwent MRI enterography of the abdomen and pelvis over a three-year period. We determined the area/volume of subcutaneous and visceral fat from the umbilical and L5 levels and calculated the visceral fat ratio (VFR = visceral fat/subcutaneous fat) and visceral fat index (VFI = visceral fat/total fat). Statistical analyses involved correlation analysis between both levels, inter-rater analysis between two investigators, and inter-platform analysis between two image-analysis platforms. Correlational analysis of 32 patients yielded significant associations for VFI (r = 0.85; p < 0.0001) and VFR (r = 0.74; p < 0.0001). Intraclass coefficients for VFI and VFR were 0.846 and 0.875 (good agreement) between investigators and 0.831 and 0.728 (good and moderate agreement) between platforms. Our study suggests that the L5 level on clinically acquired pelvic MRIs may serve as a reference point for visceral fat quantification.\n
\n\n\n\n \n\n \n \n \n \n \n Quantifying 18F-Fluorodeoxyglucose Uptake in Perianal Fistulas on PET/CT: A Retrospective Analysis.\n \n \n \n\n\n \n Huang, K.; Garuba, F.; Ganapathy, A.; Bishop, G.; Zhang, H.; Lovato, A.; Itani, M.; Viswanath, S.; Fraum, T.; Deepak, P.; and Ballard, D.\n\n\n \n\n\n\n Academic Radiology, 31. 2024.\n \n\n\n\n
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\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{Huang2024,\n abstract = {Rationale and Objectives: The use of <sup>18</sup>F-fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET/CT) in assessing inflammatory diseases has shown significant promise. Uptake patterns in perianal fistulas, which may be an incidental finding on PET/CT, have not been purposefully studied. Our aim was to compare FDG uptake of perianal fistulas to that of the liver and anal canal in patients who underwent PET/CT for hematologic/oncologic diagnosis or staging. Materials and Methods: We retrospectively identified patients who underwent FDG-PET/CT imaging between January 2011 and May 2023, where the report described a perianal fistula or abscess. PET/CTs of patients included in the study were retrospectively analyzed to record the maximum standardized uptake value (SUV<inf>max</inf>) of the fistula, abscess, anal canal, rectum, and liver. Fistula-to-liver and Fistula-to-anus SUV<inf>max</inf> ratios were calculated. We statistically compared FDG activity among the fistula, liver, and anal canal. We also assessed FDG activity in patients with vs. without anorectal cancer, as well as across different St. James fistula grades. Results: The study included 24 patients with identifiable fistulas. Fistula SUV<inf>max</inf> (mean = 10.8 ± 5.28) was significantly higher than both the liver (mean = 3.09 ± 0.584, p < 0.0001) and the anal canal (mean = 5.98 ± 2.63, p = 0.0005). Abscess fistula SUV<inf>max</inf> was 15.8 ± 4.91. St. James grade 1 fistulas had significantly lower SUV<inf>max</inf> compared to grades 2 and 4 (p = 0.0224 and p = 0.0295, respectively). No significant differences existed in SUV<inf>max</inf> ratios between anorectal and non-anorectal cancer groups. Conclusion: Perianal fistulas have increased FDG avidity with fistula SUV<inf>max</inf> values that are significantly higher than the anal canal.},\n author = {K.H. Huang and F.O. Garuba and A.K. Ganapathy and G.L. Bishop and H. Zhang and A. Lovato and M. Itani and S.E. Viswanath and T.J. Fraum and P. Deepak and D.H. Ballard},\n doi = {10.1016/j.acra.2023.12.020},\n issn = {18784046},\n issue = {7},\n journal = {Academic Radiology},\n keywords = {Crohn's Disease,Inflammation,MRI,PET/CT,Perianal fistula},\n title = {Quantifying <sup>18</sup>F-Fluorodeoxyglucose Uptake in Perianal Fistulas on PET/CT: A Retrospective Analysis},\n volume = {31},\n year = {2024},\n}\n\n\n Rationale and Objectives: The use of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET/CT) in assessing inflammatory diseases has shown significant promise. Uptake patterns in perianal fistulas, which may be an incidental finding on PET/CT, have not been purposefully studied. Our aim was to compare FDG uptake of perianal fistulas to that of the liver and anal canal in patients who underwent PET/CT for hematologic/oncologic diagnosis or staging. Materials and Methods: We retrospectively identified patients who underwent FDG-PET/CT imaging between January 2011 and May 2023, where the report described a perianal fistula or abscess. PET/CTs of patients included in the study were retrospectively analyzed to record the maximum standardized uptake value (SUVmax ) of the fistula, abscess, anal canal, rectum, and liver. Fistula-to-liver and Fistula-to-anus SUVmax ratios were calculated. We statistically compared FDG activity among the fistula, liver, and anal canal. We also assessed FDG activity in patients with vs. without anorectal cancer, as well as across different St. James fistula grades. Results: The study included 24 patients with identifiable fistulas. Fistula SUVmax (mean = 10.8 ± 5.28) was significantly higher than both the liver (mean = 3.09 ± 0.584, p < 0.0001) and the anal canal (mean = 5.98 ± 2.63, p = 0.0005). Abscess fistula SUVmax was 15.8 ± 4.91. St. James grade 1 fistulas had significantly lower SUVmax compared to grades 2 and 4 (p = 0.0224 and p = 0.0295, respectively). No significant differences existed in SUVmax ratios between anorectal and non-anorectal cancer groups. Conclusion: Perianal fistulas have increased FDG avidity with fistula SUVmax values that are significantly higher than the anal canal.\n
\n\n\n\n \n\n \n \n \n \n \n TOpCLASS Expert Consensus Classification of Perianal Fistulising Crohn's Disease: A Real-world Application in a Serial Fistula MRI Cohort.\n \n \n \n\n\n \n Schroeder, M.; Abushamma, S.; George, A.; Ravella, B.; Hickman, J.; Elumalai, A.; Wise, P.; Zulfiqar, M.; Ludwig, D.; Shetty, A.; Viswanath, S.; Luo, C.; Sebastian, S.; Ballard, D.; and Deepak, P.\n\n\n \n\n\n\n Journal of Crohn's and Colitis, 18. 2024.\n \n\n\n\n
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\n\n\n@article{Schroeder2024,\n abstract = {Background and Aims: Perianal fistuliing Crohn's disease [PFCD] is an aggressive phenotype of Crohn's disease defined by frequent relapses and disabling symptoms. A novel consensus classification system was recently outlined by the TOpCLASS consortium, which seeks to unify disease severity with patient-centred goals but has not yet been validated. We aimed to apply this to a real-world cohort and to identify factors that predict transition between classes over time. Methods: We identified all patients with PFCD and at least one baseline and one follow-up pelvic MRI [pMRI]. TOpCLASS classification, disease characteristics, and imaging indices were collected retrospectively at time periods corresponding with respective MRIs. Results: We identified 100 patients with PFCD, of whom 96 were assigned TOpCLASS Classes 1-2c at baseline. Most patients [78.1%] started in Class 2b, but changes in classification were observed in 52.1% of all patients. Male sex [72.0%, 46.6%, 40.0%, p = 0.03] and prior perianal surgery [52.0% vs 44.6% vs 40.0%, p = 0.02] were more frequently observed in those with improved class compared to unchanged and worsened class. Baseline pMRI indices were not associated with changes in classification; however, greater improvements in mVAI, MODIFI-CD, and PEMPAC were seen among those who improved. Linear mixed effect modelling identified only male sex [-0.31, 95% CI -0.60 to -0.02] with improvement in class. Conclusion: The TOpCLASS classification highlights the dynamic nature of PFCD over time. However, our ability to predict transitions between classes remains limited and requires prospective assessment. Improvement in MRI index scores over time was associated with a transition to lower TOpCLASS classification.},\n author = {M.K. Schroeder and S. Abushamma and A.T. George and B. Ravella and J. Hickman and A. Elumalai and P.E. Wise and M. Zulfiqar and D.R. Ludwig and A.S. Shetty and S.E. Viswanath and C. Luo and S.S. Sebastian and D.H. Ballard and P. Deepak},\n doi = {10.1093/ecco-jcc/jjae056},\n issn = {18764479},\n issue = {9},\n journal = {Journal of Crohn's and Colitis},\n keywords = {Crohn's disease,classification system,pelvic MRI,perianal fistula},\n title = {TOpCLASS Expert Consensus Classification of Perianal Fistulising Crohn's Disease: A Real-world Application in a Serial Fistula MRI Cohort},\n volume = {18},\n year = {2024},\n}\n\n\n Background and Aims: Perianal fistuliing Crohn's disease [PFCD] is an aggressive phenotype of Crohn's disease defined by frequent relapses and disabling symptoms. A novel consensus classification system was recently outlined by the TOpCLASS consortium, which seeks to unify disease severity with patient-centred goals but has not yet been validated. We aimed to apply this to a real-world cohort and to identify factors that predict transition between classes over time. Methods: We identified all patients with PFCD and at least one baseline and one follow-up pelvic MRI [pMRI]. TOpCLASS classification, disease characteristics, and imaging indices were collected retrospectively at time periods corresponding with respective MRIs. Results: We identified 100 patients with PFCD, of whom 96 were assigned TOpCLASS Classes 1-2c at baseline. Most patients [78.1%] started in Class 2b, but changes in classification were observed in 52.1% of all patients. Male sex [72.0%, 46.6%, 40.0%, p = 0.03] and prior perianal surgery [52.0% vs 44.6% vs 40.0%, p = 0.02] were more frequently observed in those with improved class compared to unchanged and worsened class. Baseline pMRI indices were not associated with changes in classification; however, greater improvements in mVAI, MODIFI-CD, and PEMPAC were seen among those who improved. Linear mixed effect modelling identified only male sex [-0.31, 95% CI -0.60 to -0.02] with improvement in class. Conclusion: The TOpCLASS classification highlights the dynamic nature of PFCD over time. However, our ability to predict transitions between classes remains limited and requires prospective assessment. Improvement in MRI index scores over time was associated with a transition to lower TOpCLASS classification.\n
\n\n\n\n \n\n \n \n \n \n \n Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.\n \n \n \n\n\n \n Muthusivarajan, R.; Celaya, A.; Yung, J.; Long, J.; Viswanath, S.; Marcus, D.; Chung, C.; and Fuentes, D.\n\n\n \n\n\n\n Medical Physics, 51. 2024.\n \n\n\n\n
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\n\n\n@article{Muthusivarajan2024,\n abstract = {Background: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. Purpose: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. Methods: We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as “better” quality (BQ) or “worse” quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. Results: For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. Conclusions: Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.},\n author = {R. Muthusivarajan and A. Celaya and J.P. Yung and J.P. Long and S.E. Viswanath and D.S. Marcus and C.C. Chung and D.T. Fuentes},\n doi = {10.1002/mp.17059},\n issn = {24734209},\n issue = {7},\n journal = {Medical Physics},\n keywords = {AI,brain tumor,deep learning,image quality,segmentation},\n title = {Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors},\n volume = {51},\n year = {2024},\n}\n\n\n Background: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. Purpose: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. Methods: We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as “better” quality (BQ) or “worse” quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. Results: For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. Conclusions: Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.\n
\n\n\n\n \n\n \n \n \n \n \n Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study.\n \n \n \n\n\n \n al-Mubarak , H.; Bane, O.; Gillingham, N.; Kyriakakos, C.; Abboud, G.; Cuevas, J.; Gonzalez, J.; Meilika, K.; Horowitz, A.; Huang, H.; Daza, J.; Fauveau, V.; Badani, K.; Viswanath, S.; Taouli, B.; and Lewis, S.\n\n\n \n\n\n\n Abdominal Radiology, 49. 2024.\n \n\n\n\n
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\n\n\n@article{,\n abstract = {Objectives: To evaluate radiomics features’ reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. Methods: 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5–0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. Results: Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8–58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3–99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67–0.75] for diagnosis of RCC vs. benign RM. Conclusion: Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. Clinical relevance: Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization. Graphical abstract: (Figure presented.)},\n author = {H.F. al-Mubarak and O. Bane and N. Gillingham and C.K. Kyriakakos and G. Abboud and J.M. Cuevas and J. Gonzalez and K.N. Meilika and A. Horowitz and H. Huang and J. Daza and V. Fauveau and K.K. Badani and S.E. Viswanath and B.A. Taouli and S.C. Lewis},\n doi = {10.1007/s00261-024-04212-z},\n issn = {23660058},\n issue = {10},\n journal = {Abdominal Radiology},\n keywords = {Clear cell renal cell carcinoma,Magnetic resonance imaging,Radiomics,Renal cell carcinoma,Renal mass,Reproducibility},\n title = {Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study},\n volume = {49},\n year = {2024},\n}\n\n\n Objectives: To evaluate radiomics features’ reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. Methods: 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5–0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. Results: Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8–58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3–99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67–0.75] for diagnosis of RCC vs. benign RM. Conclusion: Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. Clinical relevance: Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization. Graphical abstract: (Figure presented.)\n
\n\n\n\n \n\n \n \n \n \n \n Evaluating the Change in 18F-Fluorodeoxyglucose Uptake in Perianal Fistulas on PET/CT over Time: A Serial Retrospective Analysis.\n \n \n \n\n\n \n Garuba, F.; Ganapathy, A.; Huang, K.; Bishop, G.; Zhang, H.; Lovato, A.; Itani, M.; Viswanath, S.; Fraum, T.; Deepak, P.; and Ballard, D.\n\n\n \n\n\n\n Academic Radiology, 31. 2024.\n \n\n\n\n
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\n\n\n@article{Garuba2024,\n abstract = {Rationale and Objectives: Perianal fistulas on<sup>18</sup>F-fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET/CT) can be an incidental site of FDG uptake in patients undergoing PET for other indications. There are no longitudinal studies describing FDG uptake patterns in perianal fistulas. Therefore, we aimed to analyze changes in FDG uptake over time in patients with incidental perianal fistulas. Patients and Methods: Patients who underwent at least two FDG-PET/CTs between January 2011 and May 2023, with incidental perianal fistula, were retrospectively identified. We analyzed all sequential PET/CTs to determine the presence of a perianal fistula and recorded the fistula's maximum standardized uptake value (SUV<inf>max</inf>). Statistical analysis compared fistula FDG-avidity in the initial versus final PET/CT examinations and assessed the correlation between initial fistula SUV<inf>max</inf> and percent change over time. Results: The study included 15 fistulas in 14 patients, with an average of 5 PET/CT examinations per patient. The average interval between the first and last PET/CT was 24 months (range: 6–64). The average initial fistula SUV<inf>max</inf> (11.28 ± 3.81) was significantly higher than the final fistula SUV<inf>max</inf> (7.22 ± 3.99) (p = 0.0067). The fistula SUV<inf>max</inf> declined by an average of 32.01 ± 35.33% with no significant correlation between initial fistula SUV<inf>max</inf> and percent change over time (r = −0.213, p = 0.443, 95% CI −0.66–0.35). Conclusion: FDG uptake in perianal fistulas shows temporal fluctuations but follows a decreasing SUV<inf>max</inf> trend, possibly indicating a relationship with inflammatory activity. Further studies with larger cohorts paired with perianal fistula pelvic MR imaging are needed to validate these observations and their utility in guiding further management.},\n author = {F.O. Garuba and A.K. Ganapathy and K.H. Huang and G.L. Bishop and H. Zhang and A. Lovato and M. Itani and S.E. Viswanath and T.J. Fraum and P. Deepak and D.H. Ballard},\n doi = {10.1016/j.acra.2024.04.014},\n issn = {18784046},\n issue = {10},\n journal = {Academic Radiology},\n title = {Evaluating the Change in <sup>18</sup>F-Fluorodeoxyglucose Uptake in Perianal Fistulas on PET/CT over Time: A Serial Retrospective Analysis},\n volume = {31},\n year = {2024},\n}\n\n\n Rationale and Objectives: Perianal fistulas on18F-fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET/CT) can be an incidental site of FDG uptake in patients undergoing PET for other indications. There are no longitudinal studies describing FDG uptake patterns in perianal fistulas. Therefore, we aimed to analyze changes in FDG uptake over time in patients with incidental perianal fistulas. Patients and Methods: Patients who underwent at least two FDG-PET/CTs between January 2011 and May 2023, with incidental perianal fistula, were retrospectively identified. We analyzed all sequential PET/CTs to determine the presence of a perianal fistula and recorded the fistula's maximum standardized uptake value (SUVmax ). Statistical analysis compared fistula FDG-avidity in the initial versus final PET/CT examinations and assessed the correlation between initial fistula SUVmax and percent change over time. Results: The study included 15 fistulas in 14 patients, with an average of 5 PET/CT examinations per patient. The average interval between the first and last PET/CT was 24 months (range: 6–64). The average initial fistula SUVmax (11.28 ± 3.81) was significantly higher than the final fistula SUVmax (7.22 ± 3.99) (p = 0.0067). The fistula SUVmax declined by an average of 32.01 ± 35.33% with no significant correlation between initial fistula SUVmax and percent change over time (r = −0.213, p = 0.443, 95% CI −0.66–0.35). Conclusion: FDG uptake in perianal fistulas shows temporal fluctuations but follows a decreasing SUVmax trend, possibly indicating a relationship with inflammatory activity. Further studies with larger cohorts paired with perianal fistula pelvic MR imaging are needed to validate these observations and their utility in guiding further management.\n
\n\n\n\n \n\n \n \n \n \n \n MSCs mediate long-term efficacy in a Crohn’s disease model by sustained anti-inflammatory macrophage programming via efferocytosis.\n \n \n \n\n\n \n Dave, M.; Dev, A.; Somoza, R.; Zhao, N.; Viswanath, S.; Mina, P.; Chirra, P.; Obmann, V.; Mahabeleshwar, G.; Menghini, P.; Durbin-Johnson, B.; Nolta, J.; Soto, C.; Ösme, A.; Khuat, L.; Murphy, W.; Caplan, A.; and Cominelli, F.\n\n\n \n\n\n\n npj Regenerative Medicine, 9. 2024.\n \n\n\n\n
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\n\n\n@article{Dave2024,\n abstract = {Mesenchymal stem cells (MSCs) are novel therapeutics for the treatment of Crohn’s disease. However, their mechanism of action is unclear, especially in disease-relevant chronic models of inflammation. Thus, we used SAMP-1/YitFc (SAMP), a chronic and spontaneous murine model of small intestinal inflammation, to study the therapeutic effects and mechanism of action of human bone marrow-derived MSCs (hMSC). hMSC dose-dependently inhibited naïve T lymphocyte proliferation via prostaglandin E<inf>2</inf> (PGE<inf>2</inf>) secretion and reprogrammed macrophages to an anti-inflammatory phenotype. We found that the hMSCs promoted mucosal healing and immunologic response early after administration in SAMP when live hMSCs are present (until day 9) and resulted in a complete response characterized by mucosal, histological, immunologic, and radiological healing by day 28 when no live hMSCs are present. hMSCs mediate their effect via modulation of T cells and macrophages in the mesentery and mesenteric lymph nodes (mLN). Sc-RNAseq confirmed the anti-inflammatory phenotype of macrophages and identified macrophage efferocytosis of apoptotic hMSCs as a mechanism that explains their long-term efficacy. Taken together, our findings show that hMSCs result in healing and tissue regeneration in a chronic model of small intestinal inflammation and despite being short-lived, exert long-term effects via sustained anti-inflammatory programming of macrophages via efferocytosis.},\n author = {M. Dave and A. Dev and R.A. Somoza and N. Zhao and S.E. Viswanath and P.R. Mina and P.V. Chirra and V.C. Obmann and G.H. Mahabeleshwar and P. Menghini and B.P. Durbin-Johnson and J.A. Nolta and C. Soto and A. Ösme and L.T. Khuat and W.J. Murphy and A.I. Caplan and F. Cominelli},\n doi = {10.1038/s41536-024-00347-1},\n issn = {20573995},\n issue = {1},\n journal = {npj Regenerative Medicine},\n title = {MSCs mediate long-term efficacy in a Crohn’s disease model by sustained anti-inflammatory macrophage programming via efferocytosis},\n volume = {9},\n year = {2024},\n}\n\n\n Mesenchymal stem cells (MSCs) are novel therapeutics for the treatment of Crohn’s disease. However, their mechanism of action is unclear, especially in disease-relevant chronic models of inflammation. Thus, we used SAMP-1/YitFc (SAMP), a chronic and spontaneous murine model of small intestinal inflammation, to study the therapeutic effects and mechanism of action of human bone marrow-derived MSCs (hMSC). hMSC dose-dependently inhibited naïve T lymphocyte proliferation via prostaglandin E2 (PGE2 ) secretion and reprogrammed macrophages to an anti-inflammatory phenotype. We found that the hMSCs promoted mucosal healing and immunologic response early after administration in SAMP when live hMSCs are present (until day 9) and resulted in a complete response characterized by mucosal, histological, immunologic, and radiological healing by day 28 when no live hMSCs are present. hMSCs mediate their effect via modulation of T cells and macrophages in the mesentery and mesenteric lymph nodes (mLN). Sc-RNAseq confirmed the anti-inflammatory phenotype of macrophages and identified macrophage efferocytosis of apoptotic hMSCs as a mechanism that explains their long-term efficacy. Taken together, our findings show that hMSCs result in healing and tissue regeneration in a chronic model of small intestinal inflammation and despite being short-lived, exert long-term effects via sustained anti-inflammatory programming of macrophages via efferocytosis.\n
\n\n\n\n \n\n \n \n \n \n \n Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn's Disease.\n \n \n \n\n\n \n Chirra, P.; Sleiman, J.; Gandhi, N.; Gordon, I.; Hariri, M.; Baker, M.; Ottichilo, R.; Bruining, D.; Kurowski, J.; Viswanath, S.; and Rieder, F.\n\n\n \n\n\n\n Journal of Crohn's and Colitis, 18. 2024.\n \n\n\n\n
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\n\n\n@article{Chirra2024,\n abstract = {Background and Aims: Non-invasive cross-sectional imaging via magnetic resonance enterography [MRE] offers excellent accuracy for the diagnosis of stricturing complications in Crohn's disease [CD] but is limited in determining the degrees of fibrosis and inflammation within a stricture. We developed and validated a radiomics-based machine-learning model for separately characterizing the degree of histopathological inflammation and fibrosis in CD strictures and compared it to centrally read visual radiologist scoring of MRE. Methods: This single-centre, cross-sectional study included 51 CD patients [n = 34 for discovery; n = 17 for validation] with terminal ileal strictures confirmed on diagnostic MRE within 15 weeks of resection. Histopathological specimens were scored for inflammation and fibrosis and spatially linked with corresponding pre-surgical MRE sequences. Annotated stricture regions on MRE were scored visually by radiologists as well as underwent 3D radiomics-based machine learning analysis; both were evaluated against histopathology. Results: Two distinct sets of radiomic features capturing textural heterogeneity within strictures were linked with each of severe inflammation or severe fibrosis across both the discovery (area under the curve [AUC = 0.69, 0.83] and validation [AUC = 0.67, 0.78] cohorts. Radiologist visual scoring had an AUC = 0.67 for identifying severe inflammation and AUC = 0.35 for severe fibrosis. Use of combined radiomics and radiologist scoring robustly augmented identification of severe inflammation [AUC = 0.79] and modestly improved assessment of severe fibrosis [AUC = 0.79 for severe fibrosis] over individual approaches. Conclusions: Radiomic features of CD strictures on MRE can accurately identify severe histopathological inflammation and severe histopathological fibrosis, as well as augment performance of the radiologist visual scoring in stricture characterization.},\n author = {P.V. Chirra and J. Sleiman and N.S. Gandhi and I.O. Gordon and M. Hariri and M.E. Baker and R. Ottichilo and D.H. Bruining and J.A. Kurowski and S.E. Viswanath and F. Rieder},\n doi = {10.1093/ecco-jcc/jjae073},\n issn = {18764479},\n issue = {10},\n journal = {Journal of Crohn's and Colitis},\n keywords = {Stenosis,extracellular matrix,fibrostenosis,therapy},\n title = {Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn's Disease},\n volume = {18},\n year = {2024},\n}\n\n\n Background and Aims: Non-invasive cross-sectional imaging via magnetic resonance enterography [MRE] offers excellent accuracy for the diagnosis of stricturing complications in Crohn's disease [CD] but is limited in determining the degrees of fibrosis and inflammation within a stricture. We developed and validated a radiomics-based machine-learning model for separately characterizing the degree of histopathological inflammation and fibrosis in CD strictures and compared it to centrally read visual radiologist scoring of MRE. Methods: This single-centre, cross-sectional study included 51 CD patients [n = 34 for discovery; n = 17 for validation] with terminal ileal strictures confirmed on diagnostic MRE within 15 weeks of resection. Histopathological specimens were scored for inflammation and fibrosis and spatially linked with corresponding pre-surgical MRE sequences. Annotated stricture regions on MRE were scored visually by radiologists as well as underwent 3D radiomics-based machine learning analysis; both were evaluated against histopathology. Results: Two distinct sets of radiomic features capturing textural heterogeneity within strictures were linked with each of severe inflammation or severe fibrosis across both the discovery (area under the curve [AUC = 0.69, 0.83] and validation [AUC = 0.67, 0.78] cohorts. Radiologist visual scoring had an AUC = 0.67 for identifying severe inflammation and AUC = 0.35 for severe fibrosis. Use of combined radiomics and radiologist scoring robustly augmented identification of severe inflammation [AUC = 0.79] and modestly improved assessment of severe fibrosis [AUC = 0.79 for severe fibrosis] over individual approaches. Conclusions: Radiomic features of CD strictures on MRE can accurately identify severe histopathological inflammation and severe histopathological fibrosis, as well as augment performance of the radiologist visual scoring in stricture characterization.\n
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\n \n \n\n \n \n \n \n \n Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study.\n \n \n \n\n\n \n DeSilvio, T.; Antunes, J.; Bera, K.; Chirra, P.; Le, H.; Liska, D.; Stein, S.; Marderstein, E.; Hall, W.; Paspulati, R.; Gollamudi, J.; Purysko, A.; and Viswanath, S.\n\n\n \n\n\n\n Frontiers in Medicine, 10. 2023.\n \n\n\n\n
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\n\n\n@article{DeSilvio2023,\n abstract = {Introduction: For locally advanced rectal cancers, in vivo radiological evaluation of tumor extent and regression after neoadjuvant therapy involves implicit visual identification of rectal structures on magnetic resonance imaging (MRI). Additionally, newer image-based, computational approaches (e.g., radiomics) require more detailed and precise annotations of regions such as the outer rectal wall, lumen, and perirectal fat. Manual annotations of these regions, however, are highly laborious and time-consuming as well as subject to inter-reader variability due to tissue boundaries being obscured by treatment-related changes (e.g., fibrosis, edema). Methods: This study presents the application of U-Net deep learning models that have been uniquely developed with region-specific context to automatically segment each of the outer rectal wall, lumen, and perirectal fat regions on post-treatment, T<inf>2</inf>-weighted MRI scans. Results: In multi-institutional evaluation, region-specific U-Nets (wall Dice = 0.920, lumen Dice = 0.895) were found to perform comparably to multiple readers (wall inter-reader Dice = 0.946, lumen inter-reader Dice = 0.873). Additionally, when compared to a multi-class U-Net, region-specific U-Nets yielded an average 20% improvement in Dice scores for segmenting each of the wall, lumen, and fat; even when tested on T<inf>2</inf>-weighted MRI scans that exhibited poorer image quality, or from a different plane, or were accrued from an external institution. Discussion: Developing deep learning segmentation models with region-specific context may thus enable highly accurate, detailed annotations for multiple rectal structures on post-chemoradiation T<inf>2</inf>-weighted MRI scans, which is critical for improving evaluation of tumor extent in vivo and building accurate image-based analytic tools for rectal cancers.},\n author = {T. DeSilvio and J.T. Antunes and K. Bera and P.V. Chirra and H. Le and D. Liska and S.L. Stein and E.L. Marderstein and W.A. Hall and R.M. Paspulati and J. Gollamudi and A.S. Purysko and S.E. Viswanath},\n doi = {10.3389/fmed.2023.1149056},\n issn = {2296858X},\n journal = {Frontiers in Medicine},\n keywords = {MRI,U-Net,artificial intelligence (AI),deep learning,rectal cancer (RC),segmentation},\n title = {Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study},\n volume = {10},\n year = {2023},\n}\n\n\n Introduction: For locally advanced rectal cancers, in vivo radiological evaluation of tumor extent and regression after neoadjuvant therapy involves implicit visual identification of rectal structures on magnetic resonance imaging (MRI). Additionally, newer image-based, computational approaches (e.g., radiomics) require more detailed and precise annotations of regions such as the outer rectal wall, lumen, and perirectal fat. Manual annotations of these regions, however, are highly laborious and time-consuming as well as subject to inter-reader variability due to tissue boundaries being obscured by treatment-related changes (e.g., fibrosis, edema). Methods: This study presents the application of U-Net deep learning models that have been uniquely developed with region-specific context to automatically segment each of the outer rectal wall, lumen, and perirectal fat regions on post-treatment, T2 -weighted MRI scans. Results: In multi-institutional evaluation, region-specific U-Nets (wall Dice = 0.920, lumen Dice = 0.895) were found to perform comparably to multiple readers (wall inter-reader Dice = 0.946, lumen inter-reader Dice = 0.873). Additionally, when compared to a multi-class U-Net, region-specific U-Nets yielded an average 20% improvement in Dice scores for segmenting each of the wall, lumen, and fat; even when tested on T2 -weighted MRI scans that exhibited poorer image quality, or from a different plane, or were accrued from an external institution. Discussion: Developing deep learning segmentation models with region-specific context may thus enable highly accurate, detailed annotations for multiple rectal structures on post-chemoradiation T2 -weighted MRI scans, which is critical for improving evaluation of tumor extent in vivo and building accurate image-based analytic tools for rectal cancers.\n
\n\n\n\n \n\n \n \n \n \n \n Integrating Multi-Plane and Multi-Region Radiomic Features to Predict Pathologic Response to Neoadjuvant Chemoradiation in Rectal Cancers via Pre-Treatment MRI.\n \n \n \n\n\n \n DeSilvio, T.; Bao, L.; Seth, D.; Chirra, P.; Singh, S.; Sridharan, A.; Labbad, M.; Bingmer, K.; Jodeh, D.; Marderstein, E.; Paspulati, R.; Liska, D.; Friedman, K.; Krishnamurthi, S.; Stein, S.; Purysko, A.; and Viswanath, S.\n\n\n \n\n\n\n 2023.\n \n\n\n\n
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\n\n\n@misc{DeSilvio2023,\n abstract = {Radiomic analysis has shown significant potential for predicting treatment response to neoadjuvant therapy in rectal cancers via routine MRI, though primarily based off a single acquisition plane or single region of interest. To exploit intuitive clinical and biological aspects of tumor extent on MRI, we present a novel multi-plane, multi-region radiomics framework to more comprehensively characterize and interrogate treatment response on MRI. Our framework was evaluated on a cohort of 71 T<inf>2</inf>-weighted axial and coronal MRIs from patients diagnosed with rectal cancer and who underwent chemoradiation. 2D radiomic features were extracted from three regions of interest (tumor, fat proximal to tumor, and perirectal fat) across axial and coronal planes, with a two-stage feature selection scheme designed to identify descriptors associated with pathologic complete response. When evaluated via a quadratic discriminant analysis classifier, our multi-plane, multi-region radiomics model outperformed single-plane or single-region feature sets with an area under the ROC curve (AUC) of 0.80 ± 0.03 in discovery and AUC=0.65 in hold-out validation. Uniquely, the optimal feature set comprised descriptors from across multiple planes (axial, coronal) as well as multiple regions (tumor, proximal fat, perirectal fat). Our multi-plane, multi-region radiomics framework may thus enable more comprehensive phenotyping of treatment response on MRI, potentially finding application for improved personalization of therapeutic and surgical interventions in rectal cancers.},\n author = {T. DeSilvio and L. Bao and D. Seth and P.V. Chirra and S. Singh and A. Sridharan and M. Labbad and K.E. Bingmer and D.S. Jodeh and E.L. Marderstein and R.M. Paspulati and D. Liska and K.A. Friedman and S.S. Krishnamurthi and S.L. Stein and A.S. Purysko and S.E. Viswanath},\n doi = {10.1117/12.2655787},\n isbn = {9781510660373},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {MRI,multi-plane,radiomics,rectal cancer,treatment response},\n title = {Integrating Multi-Plane and Multi-Region Radiomic Features to Predict Pathologic Response to Neoadjuvant Chemoradiation in Rectal Cancers via Pre-Treatment MRI},\n volume = {12466},\n year = {2023},\n}\n\n\n Radiomic analysis has shown significant potential for predicting treatment response to neoadjuvant therapy in rectal cancers via routine MRI, though primarily based off a single acquisition plane or single region of interest. To exploit intuitive clinical and biological aspects of tumor extent on MRI, we present a novel multi-plane, multi-region radiomics framework to more comprehensively characterize and interrogate treatment response on MRI. Our framework was evaluated on a cohort of 71 T2 -weighted axial and coronal MRIs from patients diagnosed with rectal cancer and who underwent chemoradiation. 2D radiomic features were extracted from three regions of interest (tumor, fat proximal to tumor, and perirectal fat) across axial and coronal planes, with a two-stage feature selection scheme designed to identify descriptors associated with pathologic complete response. When evaluated via a quadratic discriminant analysis classifier, our multi-plane, multi-region radiomics model outperformed single-plane or single-region feature sets with an area under the ROC curve (AUC) of 0.80 ± 0.03 in discovery and AUC=0.65 in hold-out validation. Uniquely, the optimal feature set comprised descriptors from across multiple planes (axial, coronal) as well as multiple regions (tumor, proximal fat, perirectal fat). Our multi-plane, multi-region radiomics framework may thus enable more comprehensive phenotyping of treatment response on MRI, potentially finding application for improved personalization of therapeutic and surgical interventions in rectal cancers.\n
\n\n\n\n \n\n \n \n \n \n \n Integrating Radiomics with Clinicoradiological Scoring Can Predict High-Risk Patients Who Need Surgery in Crohn’s Disease: A Pilot Study.\n \n \n \n\n\n \n Chirra, P.; Sharma, A.; Bera, K.; Cohn, H.; Kurowski, J.; Amann, K.; Rivero, M.; Madabhushi, A.; Lu, C.; Paspulati, R.; Stein, S.; Katz, J.; Viswanath, S.; and Dave, M.\n\n\n \n\n\n\n Inflammatory Bowel Diseases, 29. 2023.\n \n\n\n\n
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\n\n\n@article{Chirra2023,\n abstract = {Background: Early identification of Crohn’s disease (CD) patients at risk for complications could enable targeted surgical referral, but routine magnetic resonance enterography (MRE) has not been definitively correlated with need for surgery. Our objective was to identify computer-extracted image (radiomic) features from MRE associated with risk of surgery in CD and combine them with clinical and radiological assessments to predict time to intervention. Methods: This was a retrospective single-center pilot study of CD patients who had an MRE within 3 months prior to initiating medical therapy. Radiomic features were extracted from annotated terminal ileum regions on MRE and combined with clinical variables and radiological assessment (via Simplified Magnetic Resonance Index of Activity scoring for wall thickening, edema, fat stranding, ulcers) in a random forest classifier. The primary endpoint was high- and low-risk groups based on need for surgery within 1 year of MRE. The secondary endpoint was time to surgery after treatment. Results: Eight radiomic features capturing localized texture heterogeneity within the terminal ileum were significantly associated with risk of surgery within 1 year of treatment (P < .05); yielding a discovery cohort area under the receiver-operating characteristic curve of 0.67 (n = 50) and validation cohort area under the receiver-operating characteristic curve of 0.74 (n = 23). Kaplan-Meier analysis of radiomic features together with clinical variables and Simplified Magnetic Resonance Index of Activity scores yielded the best hazard ratio of 4.13 (P = (7.6 × 10<sup>-6</sup>) and concordance index of 0.71 in predicting time to surgery after MRE. Conclusions: Radiomic features on MRE may be associated with risk of surgery in CD, and in combination with clinicoradiological scoring can yield an accurate prognostic model for time to surgery.},\n author = {P.V. Chirra and A.N. Sharma and K. Bera and H.M. Cohn and J.A. Kurowski and K. Amann and M.J. Rivero and A. Madabhushi and C. Lu and R.M. Paspulati and S.L. Stein and J.A. Katz and S.E. Viswanath and M. Dave},\n doi = {10.1093/ibd/izac211},\n issn = {15364844},\n issue = {3},\n journal = {Inflammatory Bowel Diseases},\n keywords = {Crohn’s disease,MRE,prognosis,radiomics,sMARIA,surgery},\n title = {Integrating Radiomics with Clinicoradiological Scoring Can Predict High-Risk Patients Who Need Surgery in Crohn’s Disease: A Pilot Study},\n volume = {29},\n year = {2023},\n}\n\n\n Background: Early identification of Crohn’s disease (CD) patients at risk for complications could enable targeted surgical referral, but routine magnetic resonance enterography (MRE) has not been definitively correlated with need for surgery. Our objective was to identify computer-extracted image (radiomic) features from MRE associated with risk of surgery in CD and combine them with clinical and radiological assessments to predict time to intervention. Methods: This was a retrospective single-center pilot study of CD patients who had an MRE within 3 months prior to initiating medical therapy. Radiomic features were extracted from annotated terminal ileum regions on MRE and combined with clinical variables and radiological assessment (via Simplified Magnetic Resonance Index of Activity scoring for wall thickening, edema, fat stranding, ulcers) in a random forest classifier. The primary endpoint was high- and low-risk groups based on need for surgery within 1 year of MRE. The secondary endpoint was time to surgery after treatment. Results: Eight radiomic features capturing localized texture heterogeneity within the terminal ileum were significantly associated with risk of surgery within 1 year of treatment (P < .05); yielding a discovery cohort area under the receiver-operating characteristic curve of 0.67 (n = 50) and validation cohort area under the receiver-operating characteristic curve of 0.74 (n = 23). Kaplan-Meier analysis of radiomic features together with clinical variables and Simplified Magnetic Resonance Index of Activity scores yielded the best hazard ratio of 4.13 (P = (7.6 × 10-6) and concordance index of 0.71 in predicting time to surgery after MRE. Conclusions: Radiomic features on MRE may be associated with risk of surgery in CD, and in combination with clinicoradiological scoring can yield an accurate prognostic model for time to surgery.\n
\n\n\n\n \n\n \n \n \n \n \n Deep learning of renal scans in children with antenatal hydronephrosis.\n \n \n \n\n\n \n Weaver, J.; Logan, J.; Broms, R.; Antony, M.; Rickard, M.; Erdman, L.; Edwins, R.; Pominville, R.; Hannick, J.; Woo, L.; Viteri, B.; D'Souza, N.; Viswanath, S.; Flask, C.; Lorenzo, A.; Fan, Y.; and Tasian, G.\n\n\n \n\n\n\n Journal of Pediatric Urology, 19. 2023.\n \n\n\n\n
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\n\n\n@article{Weaver2023,\n abstract = {Introduction: Antenatal hydronephrosis (ANH) is one of the most common anomalies identified on prenatal ultrasound, found in up to 4.5% of all pregnancies. Children with ANH are surveilled with repeated renal ultrasound and when there is high suspicion for a ureteropelvic junction obstruction on renal ultrasound, a mercaptuacetyltriglycerine (MAG3) Lasix renal scan is performed to evaluate for obstruction. However, the challenging interpretation of MAG3 renal scans places patients at risk of misdiagnosis. Objective: Our objective was to analyze MAG3 renal scans using machine learning to predict renal complications. We hypothesized that our deep learning model would extract features from MAG3 renal scans that can predict renal complications in children with ANH. Study design: We performed a case-control study of MAG3 studies drawn from a population of children with ANH concerning for ureteropelvic junction obstruction evaluated at our institution from January 2009 until June of 2021. The outcome was renal complications that occur ≥6 months after an equivocal MAG-3 renal scan. We created two machine learning models: a deep learning model using the radiotracer concentration versus time data from the kidney of interest and a random forest model created using clinical data. The performance of the models was assessed using measures of diagnostic accuracy. Results: We identified 152 eligible patients with available images of which 62 were cases and 90 were controls. The deep learning model predicted future renal complications with an overall accuracy of 73% (95% confidence inteveral [CI] 68–76%) and an AUC of 0.78 (95% CI 0.7, 0.84). The random forest model had an accuracy of 62% (95% CI 60–66%) and an AUC of 0.67 (95% CI. 0 64, 0.72) Discussion: Our deep learning model predicted patients at high risk of developing renal complications following an equivocal renal scan and discriminate those at low risk with moderately high accuracy (73%). The deep learning model outperformed the clinical model built from clinical features classically used by urologists for surgical decision making. Conclusion: Our models have the potential to influence clinical decision making by providing supplemental analytical data from MAG3 scans that would not otherwise be available to urologists. Future multi-institutional retrospective and prospective trials are needed to validate our model.[Formula presented]},\n author = {J.K. Weaver and J.R. Logan and R. Broms and M.B. Antony and M. Rickard and L.E. Erdman and R.C. Edwins and R.J. Pominville and J.H. Hannick and L.L. Woo and B. Viteri and N. D'Souza and S.E. Viswanath and C.A. Flask and A.J. Lorenzo and Y. Fan and G.E. Tasian},\n doi = {10.1016/j.jpurol.2022.12.017},\n issn = {18734898},\n issue = {5},\n journal = {Journal of Pediatric Urology},\n keywords = {Antenatal hydronephrosis,MAG3 Renal scan,Machine learning,Ureteropelvic junction obstruction},\n title = {Deep learning of renal scans in children with antenatal hydronephrosis},\n volume = {19},\n year = {2023},\n}\n\n\n Introduction: Antenatal hydronephrosis (ANH) is one of the most common anomalies identified on prenatal ultrasound, found in up to 4.5% of all pregnancies. Children with ANH are surveilled with repeated renal ultrasound and when there is high suspicion for a ureteropelvic junction obstruction on renal ultrasound, a mercaptuacetyltriglycerine (MAG3) Lasix renal scan is performed to evaluate for obstruction. However, the challenging interpretation of MAG3 renal scans places patients at risk of misdiagnosis. Objective: Our objective was to analyze MAG3 renal scans using machine learning to predict renal complications. We hypothesized that our deep learning model would extract features from MAG3 renal scans that can predict renal complications in children with ANH. Study design: We performed a case-control study of MAG3 studies drawn from a population of children with ANH concerning for ureteropelvic junction obstruction evaluated at our institution from January 2009 until June of 2021. The outcome was renal complications that occur ≥6 months after an equivocal MAG-3 renal scan. We created two machine learning models: a deep learning model using the radiotracer concentration versus time data from the kidney of interest and a random forest model created using clinical data. The performance of the models was assessed using measures of diagnostic accuracy. Results: We identified 152 eligible patients with available images of which 62 were cases and 90 were controls. The deep learning model predicted future renal complications with an overall accuracy of 73% (95% confidence inteveral [CI] 68–76%) and an AUC of 0.78 (95% CI 0.7, 0.84). The random forest model had an accuracy of 62% (95% CI 60–66%) and an AUC of 0.67 (95% CI. 0 64, 0.72) Discussion: Our deep learning model predicted patients at high risk of developing renal complications following an equivocal renal scan and discriminate those at low risk with moderately high accuracy (73%). The deep learning model outperformed the clinical model built from clinical features classically used by urologists for surgical decision making. Conclusion: Our models have the potential to influence clinical decision making by providing supplemental analytical data from MAG3 scans that would not otherwise be available to urologists. Future multi-institutional retrospective and prospective trials are needed to validate our model.[Formula presented]\n
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\n \n \n\n \n \n \n \n \n Identifying radiomic features associated with disease activity, patient outcomes, and serum phenotypes in pediatric Crohn's disease via MRI.\n \n \n \n\n\n \n Chirra, P.; Muchhala, A.; Amann, K.; Krishnan, K.; Kurowski, J.; and Viswanath, S.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
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\n\n\n@misc{Chirra2022,\n abstract = {Pediatric Crohn's disease (pCD) is a chronic relapsing-remitting inflammatory disease of the gastrointestinal tract, where there is a significant need for non-invasive comprehensive markers to accurately target clinical interventions. While Magnetic Resonance Enterography (MRE) is often used to localize pCD in vivo, it is still limited in predicting treatment response and capturing pCD phenotypes. The goal of this study was to identify radiomic features on baseline MRE associated with disease activity and treatment outcomes in pCD, as well as investigate potential associations of radiomics with serum-based pCD subtypes. Baseline MRE scans were acquired from 45 pediatric subjects (including healthy controls and pCD patients) where the latter was further sub-grouped into responders (stable after treatment) and non-responders (required surgery or had active disease 2+ years after treatment initiation). Radiomic features were extracted from the terminal ileum on a per-voxel basis from MRE and evaluated via a multi-stage feature selection scheme for identifying disease presence and patient outcomes separately. A Random Forest (RF) classifier achieved an area under the ROC curve (AUC) of 0.83 in distinguishing diseased patients from healthy subjects and an AUC of 0.85 in distinguishing nonresponders from responders; in leave-one-out cross-validation. Top-ranked Gabor and Laws radiomic features were found to be significantly correlated with serum pCD phenotypes including anemia, inflammation risk, vitamin deficiency, and immune activity. Radiomic features may therefore offer the ability to better characterize pCD phenotypes and predict patient outcomes, which could then be effectively treated via targeted interventions. },\n author = {P.V. Chirra and A. Muchhala and K. Amann and K. Krishnan and J.A. Kurowski and S.E. Viswanath},\n doi = {10.1117/12.2613599},\n isbn = {9781510649439},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Crohn's disease,Radiomics,prediction,prognosis,serum markers,small bowel},\n title = {Identifying radiomic features associated with disease activity, patient outcomes, and serum phenotypes in pediatric Crohn's disease via MRI},\n volume = {12034},\n year = {2022},\n}\n\n\n Pediatric Crohn's disease (pCD) is a chronic relapsing-remitting inflammatory disease of the gastrointestinal tract, where there is a significant need for non-invasive comprehensive markers to accurately target clinical interventions. While Magnetic Resonance Enterography (MRE) is often used to localize pCD in vivo, it is still limited in predicting treatment response and capturing pCD phenotypes. The goal of this study was to identify radiomic features on baseline MRE associated with disease activity and treatment outcomes in pCD, as well as investigate potential associations of radiomics with serum-based pCD subtypes. Baseline MRE scans were acquired from 45 pediatric subjects (including healthy controls and pCD patients) where the latter was further sub-grouped into responders (stable after treatment) and non-responders (required surgery or had active disease 2+ years after treatment initiation). Radiomic features were extracted from the terminal ileum on a per-voxel basis from MRE and evaluated via a multi-stage feature selection scheme for identifying disease presence and patient outcomes separately. A Random Forest (RF) classifier achieved an area under the ROC curve (AUC) of 0.83 in distinguishing diseased patients from healthy subjects and an AUC of 0.85 in distinguishing nonresponders from responders; in leave-one-out cross-validation. Top-ranked Gabor and Laws radiomic features were found to be significantly correlated with serum pCD phenotypes including anemia, inflammation risk, vitamin deficiency, and immune activity. Radiomic features may therefore offer the ability to better characterize pCD phenotypes and predict patient outcomes, which could then be effectively treated via targeted interventions. \n
\n\n\n\n \n\n \n \n \n \n \n Deep hybrid convolutional wavelet networks: Application to predicting response to chemoradiation in rectal cancers via MRI.\n \n \n \n\n\n \n Sadri, A.; DeSilvio, T.; Chirra, P.; Purysko, A.; Paspulati, R.; Friedman, K.; Krishnamurthi, S.; Liska, D.; Stein, S.; and Viswanath, S.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
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\n\n\n@misc{Sadri2022,\n abstract = {With increasing promise of radiomics and deep learning approaches in capturing subtle patterns associated with disease response on routine MRI, there is an opportunity to more closely combine components from both approaches within a single architecture. We present a novel approach to integrating multi-scale, multi-oriented wavelet networks (WN) into a convolutional neural network (CNN) architecture, termed a deep hybrid convolutional wavelet network (DHCWN). The proposed model comprises the wavelet neurons (wavelons) that use the shift and scale parameters of a mother wavelet function as its building units. Whereas the activation functions in a typical CNN are fixed and monotonic (e.g. ReLU), the activation functions of the proposed DHCWN are wavelet functions that are flexible and significantly more stable during optimization. The proposed DHCWN was evaluated using a multi-institutional cohort of 153 pre-Treatment rectal cancer MRI scans to predict pathologic response to neoadjuvant chemoradiation. When compared to typical CNN and a multilayer wavelet perceptron (DWN-MLP) 2D and 3D architectures, our novel DHCWN yielded significantly better performance in predicting pathologic complete response (achieving a maximum accuracy of 91.23% and a maximum AUC of 0.79), across multi-institutional discovery and hold-out validation cohorts. Interpretability evaluation of all three architectures via Grad-CAM and Shapley visualizations revealed DHCWNs best captured complex texture patterns within tumor regions on MRI as associated with pathologic complete response classification. The proposed DHCWN thus offers a significantly more extensible, interpretable, and integrated solution for characterizing predictive signatures via routine imaging data. },\n author = {A.R. Sadri and T. DeSilvio and P.V. Chirra and A.S. Purysko and R.M. Paspulati and K.A. Friedman and S.S. Krishnamurthi and D. Liska and S.L. Stein and S.E. Viswanath},\n doi = {10.1117/12.2613035},\n isbn = {9781510649415},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Convolutional Neural Network,Deep Learning,MRI,Wavelet Decomposition,Wavelet Network},\n title = {Deep hybrid convolutional wavelet networks: Application to predicting response to chemoradiation in rectal cancers via MRI},\n volume = {12033},\n year = {2022},\n}\n\n\n With increasing promise of radiomics and deep learning approaches in capturing subtle patterns associated with disease response on routine MRI, there is an opportunity to more closely combine components from both approaches within a single architecture. We present a novel approach to integrating multi-scale, multi-oriented wavelet networks (WN) into a convolutional neural network (CNN) architecture, termed a deep hybrid convolutional wavelet network (DHCWN). The proposed model comprises the wavelet neurons (wavelons) that use the shift and scale parameters of a mother wavelet function as its building units. Whereas the activation functions in a typical CNN are fixed and monotonic (e.g. ReLU), the activation functions of the proposed DHCWN are wavelet functions that are flexible and significantly more stable during optimization. The proposed DHCWN was evaluated using a multi-institutional cohort of 153 pre-Treatment rectal cancer MRI scans to predict pathologic response to neoadjuvant chemoradiation. When compared to typical CNN and a multilayer wavelet perceptron (DWN-MLP) 2D and 3D architectures, our novel DHCWN yielded significantly better performance in predicting pathologic complete response (achieving a maximum accuracy of 91.23% and a maximum AUC of 0.79), across multi-institutional discovery and hold-out validation cohorts. Interpretability evaluation of all three architectures via Grad-CAM and Shapley visualizations revealed DHCWNs best captured complex texture patterns within tumor regions on MRI as associated with pathologic complete response classification. The proposed DHCWN thus offers a significantly more extensible, interpretable, and integrated solution for characterizing predictive signatures via routine imaging data. \n
\n\n\n\n \n\n \n \n \n \n \n Residual Wavelon Convolutional Networks for Characterization of Disease Response on MRI.\n \n \n \n\n\n \n Sadri, A.; DeSilvio, T.; Chirra, P.; Singh, S.; and Viswanath, S.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
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\n\n\n@misc{Sadri2022,\n abstract = {Wavelets have shown significant promise for medical image decomposition and artifact pre-processing by representing inputs via shifted and scaled components of a specified mother wavelet function. However, wavelets could also be leveraged within deep neural networks as activation functions for neurons (called wavelons) in the hidden layer. Integrating wavelons into a convolutional neural network architecture (termed a “wavelon network” (WN)) offers additional flexibility and stability during optimization, but the resulting model complexity has caused it to be limited to low-dimensional applications. Towards addressing these issues, we present the Residual Wavelon Convolutional Network (RWCN), a novel integrated WN architecture that employs weighted skip connections (to enable residual learning) together with image convolutions and wavelet activation functions to more efficiently capture high-dimensional disease response-specific patterns from medical imaging data. In addition to developing the analytical basis for wavelet activation functions as used in this work, we implemented RWCNs by adapting the popular VGG and ResNet architectures. Evaluation was conducted within three different challenging clinical problems: (a) predicting pathologic complete response (pCR) to neoadjuvant chemoradiation via 153 pre-treatment T2-weighted (T2w) MRI scans in rectal cancers, (b) evaluating pCR after chemoradiation via 100 post-treatment T2w MRIs in rectal cancers, as well as (c) risk stratifying patients who will or will not require surgery after aggressive medication in Crohn’s disease using 73 baseline MRI scans. In comparison to 4 state-of-the-art alternative models (VGG-16, VGG-19, ResNet-18, ResNet-50), RWCN architectures yielded significantly improved and more efficient classifier performance on unseen data in multi-institutional validation cohorts (hold-out accuracies of 0.82, 0.85, and 0.88, respectively).},\n author = {A.R. Sadri and T. DeSilvio and P.V. Chirra and S. Singh and S.E. Viswanath},\n doi = {10.1007/978-3-031-16437-8_35},\n isbn = {9783031164361},\n issn = {16113349},\n journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n keywords = {Convolutional neural network,Deep learning,Disease response,Residual learning,Wavelet network},\n title = {Residual Wavelon Convolutional Networks for Characterization of Disease Response on MRI},\n volume = {13433 LNCS},\n year = {2022},\n}\n\n\n Wavelets have shown significant promise for medical image decomposition and artifact pre-processing by representing inputs via shifted and scaled components of a specified mother wavelet function. However, wavelets could also be leveraged within deep neural networks as activation functions for neurons (called wavelons) in the hidden layer. Integrating wavelons into a convolutional neural network architecture (termed a “wavelon network” (WN)) offers additional flexibility and stability during optimization, but the resulting model complexity has caused it to be limited to low-dimensional applications. Towards addressing these issues, we present the Residual Wavelon Convolutional Network (RWCN), a novel integrated WN architecture that employs weighted skip connections (to enable residual learning) together with image convolutions and wavelet activation functions to more efficiently capture high-dimensional disease response-specific patterns from medical imaging data. In addition to developing the analytical basis for wavelet activation functions as used in this work, we implemented RWCNs by adapting the popular VGG and ResNet architectures. Evaluation was conducted within three different challenging clinical problems: (a) predicting pathologic complete response (pCR) to neoadjuvant chemoradiation via 153 pre-treatment T2-weighted (T2w) MRI scans in rectal cancers, (b) evaluating pCR after chemoradiation via 100 post-treatment T2w MRIs in rectal cancers, as well as (c) risk stratifying patients who will or will not require surgery after aggressive medication in Crohn’s disease using 73 baseline MRI scans. In comparison to 4 state-of-the-art alternative models (VGG-16, VGG-19, ResNet-18, ResNet-50), RWCN architectures yielded significantly improved and more efficient classifier performance on unseen data in multi-institutional validation cohorts (hold-out accuracies of 0.82, 0.85, and 0.88, respectively).\n
\n\n\n\n \n\n \n \n \n \n \n Challenges in ensuring the generalizability of image quantitation methods for MRI.\n \n \n \n\n\n \n Keenan, K.; Delfino, J.; Jordanova, K.; Poorman, M.; Chirra, P.; Chaudhari, A.; Baeßler, B.; Winfield, J.; Viswanath, S.; and DeSouza, N.\n\n\n \n\n\n\n Medical Physics, 49. 2022.\n \n\n\n\n
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\n\n\n@article{Keenan2022,\n abstract = {Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.},\n author = {K.E. Keenan and J.G. Delfino and K.V. Jordanova and M.E. Poorman and P.V. Chirra and A.S. Chaudhari and B. Baeßler and J.M. Winfield and S.E. Viswanath and N.M. DeSouza},\n doi = {10.1002/mp.15195},\n issn = {24734209},\n issue = {4},\n journal = {Medical Physics},\n keywords = {magnetic resonance imaging,multiparametric MRI,quantitative MRI,radiomics},\n title = {Challenges in ensuring the generalizability of image quantitation methods for MRI},\n volume = {49},\n year = {2022},\n}\n\n\n Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.\n
\n\n\n\n \n\n \n \n \n \n \n RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment.\n \n \n \n\n\n \n Antunes, J.; Ismail, M.; Hossain, I.; Wang, Z.; Prasanna, P.; Madabhushi, A.; Tiwari, P.; and Viswanath, S.\n\n\n \n\n\n\n IEEE Journal of Biomedical and Health Informatics, 26. 2022.\n \n\n\n\n
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\n\n\n@article{Antunes2022,\n abstract = {Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic 'expression maps', we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone. },\n author = {J.T. Antunes and M. Ismail and I. Hossain and Z. Wang and P. Prasanna and A. Madabhushi and P. Tiwari and S.E. Viswanath},\n doi = {10.1109/JBHI.2022.3146778},\n issn = {21682208},\n issue = {6},\n journal = {IEEE Journal of Biomedical and Health Informatics},\n keywords = {Glioblastoma multiforme,graph organization,radiomics,rectal cancer,response characterization,tumor heterogeneity},\n title = {RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment},\n volume = {26},\n year = {2022},\n}\n\n\n Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic 'expression maps', we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone. \n
\n\n\n\n \n\n \n \n \n \n \n Editorial for “Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics”.\n \n \n \n\n\n \n Viswanath, S.\n\n\n \n\n\n\n Journal of Magnetic Resonance Imaging, 56. 2022.\n \n\n\n\n
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\n\n\n\n@article{Viswanath2022,\n author = {S.E. Viswanath},\n doi = {10.1002/jmri.28139},\n issn = {15222586},\n issue = {4},\n journal = {Journal of Magnetic Resonance Imaging},\n title = {Editorial for “Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics”},\n volume = {56},\n year = {2022},\n}\n\n\n \n\n \n \n \n \n \n Staging and Restaging of Rectal Cancer with MRI: A Pictorial Review.\n \n \n \n\n\n \n Wetzel, A.; Viswanath, S.; Görgün, E.; Özgür, İ.; Allende, D.; Liska, D.; and Purysko, A.\n\n\n \n\n\n\n Seminars in Ultrasound, CT and MRI, 43. 2022.\n \n\n\n\n
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\n\n\n@article{Wetzel2022,\n abstract = {MRI plays an integral role in the initial local staging of rectal cancer and assessment of treatment response, with the goal of treatment to minimize local recurrence. Standard treatment of rectal cancer includes surgical excision with the addition of neoadjuvant chemoradiation therapy for locally advanced disease. MRI is ideally suited for both surgical planning and risk stratification, allowing for accurate evaluation of tumor location and characteristics, T and N staging, and other MRI-specific features. The role of MRI in risk stratification continues to expand with the emergence of novel organ-sparing management options including active surveillance, minimally invasive surgery, and alternative neoadjuvant therapies. Thus, optimal MRI interpretation requires precise evaluation of the primary tumor and its relationship to surrounding structures with a familiarity of the concepts important in risk stratification and treatment management. Additionally, recognition of the imaging modality's current challenges and limitations can prevent interpretive errors and optimize its diagnostic utility. This pictorial review discusses key concepts of MRI in the initial staging of rectal cancer, assessment of treatment response, and active surveillance of disease, including a focus and discussion on current interpretive challenges and opportunities for advancement.},\n author = {A. Wetzel and S.E. Viswanath and E. Görgün and İ. Özgür and D.S. Allende and D. Liska and A.S. Purysko},\n doi = {10.1053/j.sult.2022.06.003},\n issn = {15585034},\n issue = {6},\n journal = {Seminars in Ultrasound, CT and MRI},\n title = {Staging and Restaging of Rectal Cancer with MRI: A Pictorial Review},\n volume = {43},\n year = {2022},\n}\n\n\n MRI plays an integral role in the initial local staging of rectal cancer and assessment of treatment response, with the goal of treatment to minimize local recurrence. Standard treatment of rectal cancer includes surgical excision with the addition of neoadjuvant chemoradiation therapy for locally advanced disease. MRI is ideally suited for both surgical planning and risk stratification, allowing for accurate evaluation of tumor location and characteristics, T and N staging, and other MRI-specific features. The role of MRI in risk stratification continues to expand with the emergence of novel organ-sparing management options including active surveillance, minimally invasive surgery, and alternative neoadjuvant therapies. Thus, optimal MRI interpretation requires precise evaluation of the primary tumor and its relationship to surrounding structures with a familiarity of the concepts important in risk stratification and treatment management. Additionally, recognition of the imaging modality's current challenges and limitations can prevent interpretive errors and optimize its diagnostic utility. This pictorial review discusses key concepts of MRI in the initial staging of rectal cancer, assessment of treatment response, and active surveillance of disease, including a focus and discussion on current interpretive challenges and opportunities for advancement.\n
\n\n\n\n \n\n \n \n \n \n \n Crohn's disease related strictures in cross-sectional imaging: More than meets the eye?.\n \n \n \n\n\n \n Sleiman, J.; Chirra, P.; Gandhi, N.; Baker, M.; Lu, C.; Gordon, I.; Viswanath, S.; and Rieder, F.\n\n\n \n\n\n\n United European Gastroenterology Journal, 10. 2022.\n \n\n\n\n
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\n\n\n@article{Sleiman2022,\n abstract = {Strictures in Crohn's disease (CD) are a hallmark of long-standing intestinal damage, brought about by inflammatory and non-inflammatory pathways. Understanding the complex pathophysiology related to inflammatory infiltrates, extracellular matrix deposition, as well as muscular hyperplasia is crucial to produce high-quality scoring indices for assessing CD strictures. In addition, cross-sectional imaging modalities are the primary tool for diagnosis and follow-up of strictures, especially with the initiation of anti-fibrotic therapy clinical trials. This in turn requires such modalities to both diagnose strictures with high accuracy, as well as be able to delineate the impact of each histomorphologic component on the individual stricture. We discuss the current knowledge on cross-sectional imaging modalities used for stricturing CD, with an emphasis on histomorphologic correlates, novel imaging parameters which may improve segregation between inflammatory, muscular, and fibrotic stricture components, as well as a future outlook on the role of artificial intelligence in this field of gastroenterology.},\n author = {J. Sleiman and P.V. Chirra and N.S. Gandhi and M.E. Baker and C. Lu and I.O. Gordon and S.E. Viswanath and F. Rieder},\n doi = {10.1002/ueg2.12326},\n issn = {20506414},\n issue = {10},\n journal = {United European Gastroenterology Journal},\n keywords = {Crohn's disease,computer tomography enterography,cross-sectional imaging,fibrosis,intestinal ultrasound,magnetic resonance enterography,radiomics,strictures},\n title = {Crohn's disease related strictures in cross-sectional imaging: More than meets the eye?},\n volume = {10},\n year = {2022},\n}\n\n\n Strictures in Crohn's disease (CD) are a hallmark of long-standing intestinal damage, brought about by inflammatory and non-inflammatory pathways. Understanding the complex pathophysiology related to inflammatory infiltrates, extracellular matrix deposition, as well as muscular hyperplasia is crucial to produce high-quality scoring indices for assessing CD strictures. In addition, cross-sectional imaging modalities are the primary tool for diagnosis and follow-up of strictures, especially with the initiation of anti-fibrotic therapy clinical trials. This in turn requires such modalities to both diagnose strictures with high accuracy, as well as be able to delineate the impact of each histomorphologic component on the individual stricture. We discuss the current knowledge on cross-sectional imaging modalities used for stricturing CD, with an emphasis on histomorphologic correlates, novel imaging parameters which may improve segregation between inflammatory, muscular, and fibrotic stricture components, as well as a future outlook on the role of artificial intelligence in this field of gastroenterology.\n
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\n \n \n\n \n \n \n \n \n SPARTA: An Integrated Stability, Discriminability, and Sparsity Based Radiomic Feature Selection Approach.\n \n \n \n\n\n \n Sadri, A.; Azarianpour-Esfahani, S.; Chirra, P.; Antunes, J.; Giriprakash, P. P.; Leo, P.; Madabhushi, A.; and Viswanath, S.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
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\n\n\n@misc{Sadri2021,\n abstract = {In order to ensure that a radiomics-based machine learning model will robustly generalize to new, unseen data (which may harbor significant variations compared to the discovery cohort), radiomic features are often screened for stability via test/retest or cross-site evaluation. However, as stability screening is often conducted independent of the feature selection process, the resulting feature set may not be simultaneously optimized for discriminability, stability, as well as sparsity. In this work, we present a novel radiomic feature selection approach termed SPARse sTable lAsso (SPARTA), uniquely developed to identify a highly discriminative and sparse set of features which are also stable to acquisition or institution variations. The primary contribution of this work is the integration of feature stability as a generalizable regularization term into a least absolute shrinkage and selection operator (LASSO)-based optimization function. Secondly, we utilize a unique non-convex sparse relaxation approach inspired by proximal algorithms to provide a computationally efficient convergence guarantee for our novel algorithm. SPARTA was evaluated on three different multi-institutional imaging cohorts to identify the most relevant radiomic features for distinguishing: (a) healthy from diseased lesions in 147 prostate cancer patients via T2-weighted MRI, (b) healthy subjects from Crohn’s disease patients via 170 CT enterography scans, and (c) responders and non-responders to chemoradiation in 82 rectal cancer patients via T2w MRI. When compared to 3 state-of-the-art feature selection schemes, features selected via SPARTA yielded significantly higher classifier performance on unseen data in multi-institutional validation (hold-out AUCs of 0.91, 0.91, and 0.93 in the 3 cohorts).},\n author = {A.R. Sadri and S. Azarianpour-Esfahani and P.V. Chirra and J.T. Antunes and P. Pattiam Giriprakash and P. Leo and A. Madabhushi and S.E. Viswanath},\n doi = {10.1007/978-3-030-87199-4_42},\n isbn = {9783030871987},\n issn = {16113349},\n journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n keywords = {Convex optimization,Feature selection,Feature stability},\n title = {SPARTA: An Integrated Stability, Discriminability, and Sparsity Based Radiomic Feature Selection Approach},\n volume = {12903 LNCS},\n year = {2021},\n}\n\n\n In order to ensure that a radiomics-based machine learning model will robustly generalize to new, unseen data (which may harbor significant variations compared to the discovery cohort), radiomic features are often screened for stability via test/retest or cross-site evaluation. However, as stability screening is often conducted independent of the feature selection process, the resulting feature set may not be simultaneously optimized for discriminability, stability, as well as sparsity. In this work, we present a novel radiomic feature selection approach termed SPARse sTable lAsso (SPARTA), uniquely developed to identify a highly discriminative and sparse set of features which are also stable to acquisition or institution variations. The primary contribution of this work is the integration of feature stability as a generalizable regularization term into a least absolute shrinkage and selection operator (LASSO)-based optimization function. Secondly, we utilize a unique non-convex sparse relaxation approach inspired by proximal algorithms to provide a computationally efficient convergence guarantee for our novel algorithm. SPARTA was evaluated on three different multi-institutional imaging cohorts to identify the most relevant radiomic features for distinguishing: (a) healthy from diseased lesions in 147 prostate cancer patients via T2-weighted MRI, (b) healthy subjects from Crohn’s disease patients via 170 CT enterography scans, and (c) responders and non-responders to chemoradiation in 82 rectal cancer patients via T2w MRI. When compared to 3 state-of-the-art feature selection schemes, features selected via SPARTA yielded significantly higher classifier performance on unseen data in multi-institutional validation (hold-out AUCs of 0.91, 0.91, and 0.93 in the 3 cohorts).\n
\n\n\n\n \n\n \n \n \n \n \n Adipokine Resistin Levels at Time of Pediatric Crohn Disease Diagnosis Predict Escalation to Biologic Therapy.\n \n \n \n\n\n \n Kurowski, J.; Achkar, J.; Gupta, R.; Barbur, I.; Bonfield, T.; Worley, S.; Remer, E.; Fiocchi, C.; Viswanath, S.; and Kay, M.\n\n\n \n\n\n\n Inflammatory Bowel Diseases, 27. 2021.\n \n\n\n\n
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\n\n\n@article{Kurowski2021,\n abstract = {Background: Hypertrophy of visceral adipose tissue (VAT) is a hallmark of Crohn disease (CD). The VAT produces a wide range of adipokines, biologically active factors that contribute to metabolic disorders in addition to CD pathogenesis. The study aim was to concomitantly evaluate serum adipokine profiles and VAT volumes as predictors of disease outcomes and treatment course in newly diagnosed pediatric patients with CD. Methods: Pediatric patients ages 6 to 20 years were enrolled, and their clinical data and anthropometric measurements were obtained. Adipokine levels were measured at 0, 6, and 12 months after CD diagnosis and baseline in control patients (CP). The VAT volumes were measured by magnetic resonance imaging or computed tomography imaging within 3 months of diagnosis. Results: One hundred four patients undergoing colonoscopy were prospectively enrolled: 36 diagnosed with CD and 68 CP. The serum adipokine resistin and plasminogen activator inhibitor (PAI)-1 levels were significantly higher in patients with CD at diagnosis than in CP. The VAT volume was similar between CD and CP. Baseline resistin levels at the time of diagnosis in patients with CD who were escalated to biologics was significantly higher than in those not treated using biologic therapy by 12 months (29.8 ng/mL vs 13.8 ng/mL; P = 0.004). A resistin level of ≥29.8 ng/mL at the time of diagnosis predicted escalation to biologic therapy in the first year after diagnosis with a specificity of 95% (sensitivity = 53%; area under the curve = 0.82; P = 0.015 for model with log-scale). There was a significantly greater reduction in resistin (P = 0.002) and PAI-1 (P = 0.010) at the 12-month follow-up in patients on biologics compared with patients who were not treated using biologics. Conclusions: Serum resistin levels at diagnosis of pediatric CD predict the escalation to biologic therapy at 12 months, independent of VAT volumes. Resistin and PAI-1 levels significantly improved in patients with CD after treatment using biologics compared with those not on biologics. These results suggest the utility of resistin as a predictive biomarker in pediatric CD.},\n author = {J.A. Kurowski and J.P. Achkar and R. Gupta and I. Barbur and T.L. Bonfield and S.E. Worley and E.M. Remer and C. Fiocchi and S.E. Viswanath and M.H. Kay},\n doi = {10.1093/ibd/izaa250},\n issn = {15364844},\n issue = {7},\n journal = {Inflammatory Bowel Diseases},\n keywords = {Crohn disease,adipokines,resistin,visceral adipose tissue},\n title = {Adipokine Resistin Levels at Time of Pediatric Crohn Disease Diagnosis Predict Escalation to Biologic Therapy},\n volume = {27},\n year = {2021},\n}\n\n\n Background: Hypertrophy of visceral adipose tissue (VAT) is a hallmark of Crohn disease (CD). The VAT produces a wide range of adipokines, biologically active factors that contribute to metabolic disorders in addition to CD pathogenesis. The study aim was to concomitantly evaluate serum adipokine profiles and VAT volumes as predictors of disease outcomes and treatment course in newly diagnosed pediatric patients with CD. Methods: Pediatric patients ages 6 to 20 years were enrolled, and their clinical data and anthropometric measurements were obtained. Adipokine levels were measured at 0, 6, and 12 months after CD diagnosis and baseline in control patients (CP). The VAT volumes were measured by magnetic resonance imaging or computed tomography imaging within 3 months of diagnosis. Results: One hundred four patients undergoing colonoscopy were prospectively enrolled: 36 diagnosed with CD and 68 CP. The serum adipokine resistin and plasminogen activator inhibitor (PAI)-1 levels were significantly higher in patients with CD at diagnosis than in CP. The VAT volume was similar between CD and CP. Baseline resistin levels at the time of diagnosis in patients with CD who were escalated to biologics was significantly higher than in those not treated using biologic therapy by 12 months (29.8 ng/mL vs 13.8 ng/mL; P = 0.004). A resistin level of ≥29.8 ng/mL at the time of diagnosis predicted escalation to biologic therapy in the first year after diagnosis with a specificity of 95% (sensitivity = 53%; area under the curve = 0.82; P = 0.015 for model with log-scale). There was a significantly greater reduction in resistin (P = 0.002) and PAI-1 (P = 0.010) at the 12-month follow-up in patients on biologics compared with patients who were not treated using biologics. Conclusions: Serum resistin levels at diagnosis of pediatric CD predict the escalation to biologic therapy at 12 months, independent of VAT volumes. Resistin and PAI-1 levels significantly improved in patients with CD after treatment using biologics compared with those not on biologics. These results suggest the utility of resistin as a predictive biomarker in pediatric CD.\n
\n\n\n\n \n\n \n \n \n \n \n Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters.\n \n \n \n\n\n \n Eck, B.; Chirra, P.; Muchhala, A.; Hall, S.; Bera, K.; Tiwari, P.; Madabhushi, A.; Seiberlich, N.; and Viswanath, S.\n\n\n \n\n\n\n Journal of Magnetic Resonance Imaging, 54. 2021.\n \n\n\n\n
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\n\n\n@article{Eck2021,\n abstract = {Background: Radiomic descriptors from magnetic resonance imaging (MRI) are promising for disease diagnosis and characterization but may be sensitive to differences in imaging parameters. Objective: To evaluate the repeatability and robustness of radiomic descriptors within healthy brain tissue regions on prospectively acquired MRI scans; in a test–retest setting, under controlled systematic variations of MRI acquisition parameters, and after postprocessing. Study Type: Prospective. Subjects: Fifteen healthy participants. Field Strength/Sequence: A 3.0 T, axial T<inf>2</inf>-weighted 2D turbo spin-echo pulse sequence, 181 scans acquired (2 test/retest reference scans and 12 with systematic variations in contrast weighting, resolution, and acceleration per participant; removing scans with artifacts). Assessment: One hundred and forty-six radiomic descriptors were extracted from a contiguous 2D region of white matter in each scan, before and after postprocessing. Statistical Tests: Repeatability was assessed in a test/retest setting and between manual and automated annotations for the reference scan. Robustness was evaluated between the reference scan and each group of variant scans (contrast weighting, resolution, and acceleration). Both repeatability and robustness were quantified as the proportion of radiomic descriptors that fell into distinct ranges of the concordance correlation coefficient (CCC): excellent (CCC > 0.85), good (0.7 ≤ CCC ≤ 0.85), moderate (0.5 ≤ CCC < 0.7), and poor (CCC < 0.5); for unprocessed and postprocessed scans separately. Results: Good to excellent repeatability was observed for 52% of radiomic descriptors between test/retest scans and 48% of descriptors between automated vs. manual annotations, respectively. Contrast weighting (TR/TE) changes were associated with the largest proportion of highly robust radiomic descriptors (21%, after processing). Image resolution changes resulted in the largest proportion of poorly robust radiomic descriptors (97%, before postprocessing). Postprocessing of images with only resolution/acceleration differences resulted in 73% of radiomic descriptors showing poor robustness. Data Conclusions: Many radiomic descriptors appear to be nonrobust across variations in MR contrast weighting, resolution, and acceleration, as well in test–retest settings, depending on feature formulation and postprocessing. Evidence Level: 2. Technical Efficacy: Stage 2.},\n author = {B.L. Eck and P.V. Chirra and A. Muchhala and S. Hall and K. Bera and P. Tiwari and A. Madabhushi and N. Seiberlich and S.E. Viswanath},\n doi = {10.1002/jmri.27635},\n issn = {15222586},\n issue = {3},\n journal = {Journal of Magnetic Resonance Imaging},\n keywords = {MRI,radiomics,repeatability,reproducibility,robustness},\n title = {Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters},\n volume = {54},\n year = {2021},\n}\n\n\n Background: Radiomic descriptors from magnetic resonance imaging (MRI) are promising for disease diagnosis and characterization but may be sensitive to differences in imaging parameters. Objective: To evaluate the repeatability and robustness of radiomic descriptors within healthy brain tissue regions on prospectively acquired MRI scans; in a test–retest setting, under controlled systematic variations of MRI acquisition parameters, and after postprocessing. Study Type: Prospective. Subjects: Fifteen healthy participants. Field Strength/Sequence: A 3.0 T, axial T2 -weighted 2D turbo spin-echo pulse sequence, 181 scans acquired (2 test/retest reference scans and 12 with systematic variations in contrast weighting, resolution, and acceleration per participant; removing scans with artifacts). Assessment: One hundred and forty-six radiomic descriptors were extracted from a contiguous 2D region of white matter in each scan, before and after postprocessing. Statistical Tests: Repeatability was assessed in a test/retest setting and between manual and automated annotations for the reference scan. Robustness was evaluated between the reference scan and each group of variant scans (contrast weighting, resolution, and acceleration). Both repeatability and robustness were quantified as the proportion of radiomic descriptors that fell into distinct ranges of the concordance correlation coefficient (CCC): excellent (CCC > 0.85), good (0.7 ≤ CCC ≤ 0.85), moderate (0.5 ≤ CCC < 0.7), and poor (CCC < 0.5); for unprocessed and postprocessed scans separately. Results: Good to excellent repeatability was observed for 52% of radiomic descriptors between test/retest scans and 48% of descriptors between automated vs. manual annotations, respectively. Contrast weighting (TR/TE) changes were associated with the largest proportion of highly robust radiomic descriptors (21%, after processing). Image resolution changes resulted in the largest proportion of poorly robust radiomic descriptors (97%, before postprocessing). Postprocessing of images with only resolution/acceleration differences resulted in 73% of radiomic descriptors showing poor robustness. Data Conclusions: Many radiomic descriptors appear to be nonrobust across variations in MR contrast weighting, resolution, and acceleration, as well in test–retest settings, depending on feature formulation and postprocessing. Evidence Level: 2. Technical Efficacy: Stage 2.\n
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\n \n \n\n \n \n \n \n \n Sparse Wavelet Networks.\n \n \n \n\n\n \n Sadri, A.; Celebi, M.; Rahnavard, N.; and Viswanath, S.\n\n\n \n\n\n\n IEEE Signal Processing Letters, 27. 2020.\n \n\n\n\n
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\n\n\n@article{Sadri2020,\n abstract = {A wavelet network (WN) is a feed-forward neural network that uses wavelets as activation functions for the neurons in its hidden layer. By predetermining the wavelet positions and dilations, the WN can turn into a linear regression model. The common approach for the construction of these WN families is to use least-squares type algorithms. In this letter, we propose a novel approach by formulating a WN as a sparse linear regression problem, which we call a sparse wavelet network (SWN). In this WN, the problem of calculating the unknown inner parameters of the network becomes that of finding the sparse solution of an under-determined system of linear equations. Our sparse solution algorithm is a non-convex sparse relaxation approach inspired by smoothed L0 (SL0), a distinguished sparse recovery algorithm. The proposed SWN can be applied as a tool for the prediction and identification of dynamical systems.},\n author = {A.R. Sadri and M.E. Celebi and N. Rahnavard and S.E. Viswanath},\n doi = {10.1109/LSP.2019.2959219},\n issn = {15582361},\n journal = {IEEE Signal Processing Letters},\n keywords = {Wavelet network,non-convex regularization,sparse representation,system identification},\n title = {Sparse Wavelet Networks},\n volume = {27},\n year = {2020},\n}\n\n\n A wavelet network (WN) is a feed-forward neural network that uses wavelets as activation functions for the neurons in its hidden layer. By predetermining the wavelet positions and dilations, the WN can turn into a linear regression model. The common approach for the construction of these WN families is to use least-squares type algorithms. In this letter, we propose a novel approach by formulating a WN as a sparse linear regression problem, which we call a sparse wavelet network (SWN). In this WN, the problem of calculating the unknown inner parameters of the network becomes that of finding the sparse solution of an under-determined system of linear equations. Our sparse solution algorithm is a non-convex sparse relaxation approach inspired by smoothed L0 (SL0), a distinguished sparse recovery algorithm. The proposed SWN can be applied as a tool for the prediction and identification of dynamical systems.\n
\n\n\n\n \n\n \n \n \n \n \n Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes.\n \n \n \n\n\n \n Sandino, A.; Alvarez-Jimenez, C.; Mosquera-Zamudio, A.; Viswanath, S.; and Romero, E.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
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\n\n\n@misc{Sandino2020,\n abstract = {Histopathological evaluation plays a crucial role in the process of understanding lung cancer biology. Such evaluation consists in analyzing patterns related with tissue structure and cell morphology to identify the presence of cancer and the associated subtype. This investigation presents a multi-level texture approach to differentiate the two main lung cancer subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC), by estimating global spatial patterns in terms of cell density. Such patterns correspond to texture features computed from cell density distribution in a co-occurrence frame. Results using the proposed approach achieved an accuracy of 0.72 and F-score of 0.72.},\n author = {A.A. Sandino and C. Alvarez-Jimenez and A. Mosquera-Zamudio and S.E. Viswanath and E.C. Romero},\n doi = {10.1117/12.2542360},\n isbn = {9781510634275},\n issn = {1996756X},\n journal = {Proceedings of SPIE - The International Society for Optical Engineering},\n keywords = {Cell density,Classification,Co-occurrence matrix,Histopathology,Lung cancer,Texture features},\n title = {Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes},\n volume = {11330},\n year = {2020},\n}\n\n\n Histopathological evaluation plays a crucial role in the process of understanding lung cancer biology. Such evaluation consists in analyzing patterns related with tissue structure and cell morphology to identify the presence of cancer and the associated subtype. This investigation presents a multi-level texture approach to differentiate the two main lung cancer subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC), by estimating global spatial patterns in terms of cell density. Such patterns correspond to texture features computed from cell density distribution in a co-occurrence frame. Results using the proposed approach achieved an accuracy of 0.72 and F-score of 0.72.\n
\n\n\n\n \n\n \n \n \n \n \n Texture kinetic features from pre-treatment DCE MRI for predicting pathologic tumor stage regression after neoadjuvant chemoradiation in rectal cancers.\n \n \n \n\n\n \n Nanda, S.; Antunes, J.; Bera, K.; Brady, J.; Friedman, K.; Willis, J.; Paspulati, R.; and Viswanath, S.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@misc{Nanda2020,\n abstract = {Dynamic contrast-enhanced (DCE) MRI is increasingly used to stage and evaluate rectal cancer extent in vivo in order to plan and target interventions for locally advanced tumors. The major clinical challenge faced with rectal cancers today is to personalize interventions through early identification of patients will benefit from neoadjuvant chemoradiation (nCRT) alone and who will benefit from aggressive surgery (with adjuvant radiation) instead; via baseline imaging. In this study, we evaluated texture kinetic features of rectal tumors using baseline DCE MRI scans, in order to predict pathologic tumor stage regression in response to nCRT. Our texture kinetics approach utilized a combination of texture features (from multiple DCE uptake phases) and polynomial curve fitting to uniquely quantify spatiotemporal patterns of lesion texture during contrast uptake and diffusion that were different between responders and non-responders to nCRT. We utilized a cohort of 48 rectal cancer patients for whom pre-nCRT 3 T DCE MRI was available, including pre-, early-, and delayed-enhancement phases. All DCE MRI phases were processed for motion and spatial alignment artifacts via rigid co-registration, and the tumor ROI on all 3 contrast phases was normalized with respect to non-enhancing muscle. 191 texture features were extracted from each of 3 contrast phases separately, following which each feature was plotted with respect to time to yield a feature enhancement curve. Polynomial fitting was applied to each feature enhancement curve to result in a vector of coefficients which was considered the texture kinetic representation of that feature. All 191 features were evaluated in terms of their texture kinetic representation as well as the raw feature enhancement, for predicting pathologically regressed tumor stages (ypT0-2) from non-regressed tumors (ypT3-4) via a cross-validated QDA classifier. Texture kinetics of gradient XY enhancement yielded the best overall AUC=0:762±0:053, which was significantly higher than any feature enhancement representation (best AUC=0:696±0:050). Texture kinetic representations also outperformed their corresponding raw feature enhancement representations in 54.5% of the features compared, and performed significantly worse in only 13% of the comparisons. Non-invasive guidance of interventions in rectal cancers could therefore be enhanced through the use of texture kinetic features from DCE MRI, which may better characterize spatiotemporal differences between responders and non-responders on baseline imaging.},\n author = {S. Nanda and J.T. Antunes and K. Bera and J.T. Brady and K.A. Friedman and J.E. Willis and R.M. Paspulati and S.E. Viswanath},\n doi = {10.1117/12.2552175},\n isbn = {9781510633971},\n issn = {1996756X},\n journal = {Proceedings of SPIE - The International Society for Optical Engineering},\n keywords = {Contrast enhancement,MRI,Radiomics,Rectal cancer,Response assessment,Texture kinetics,Tumor re-gression},\n title = {Texture kinetic features from pre-treatment DCE MRI for predicting pathologic tumor stage regression after neoadjuvant chemoradiation in rectal cancers},\n volume = {11315},\n year = {2020},\n}\n\n\n Dynamic contrast-enhanced (DCE) MRI is increasingly used to stage and evaluate rectal cancer extent in vivo in order to plan and target interventions for locally advanced tumors. The major clinical challenge faced with rectal cancers today is to personalize interventions through early identification of patients will benefit from neoadjuvant chemoradiation (nCRT) alone and who will benefit from aggressive surgery (with adjuvant radiation) instead; via baseline imaging. In this study, we evaluated texture kinetic features of rectal tumors using baseline DCE MRI scans, in order to predict pathologic tumor stage regression in response to nCRT. Our texture kinetics approach utilized a combination of texture features (from multiple DCE uptake phases) and polynomial curve fitting to uniquely quantify spatiotemporal patterns of lesion texture during contrast uptake and diffusion that were different between responders and non-responders to nCRT. We utilized a cohort of 48 rectal cancer patients for whom pre-nCRT 3 T DCE MRI was available, including pre-, early-, and delayed-enhancement phases. All DCE MRI phases were processed for motion and spatial alignment artifacts via rigid co-registration, and the tumor ROI on all 3 contrast phases was normalized with respect to non-enhancing muscle. 191 texture features were extracted from each of 3 contrast phases separately, following which each feature was plotted with respect to time to yield a feature enhancement curve. Polynomial fitting was applied to each feature enhancement curve to result in a vector of coefficients which was considered the texture kinetic representation of that feature. All 191 features were evaluated in terms of their texture kinetic representation as well as the raw feature enhancement, for predicting pathologically regressed tumor stages (ypT0-2) from non-regressed tumors (ypT3-4) via a cross-validated QDA classifier. Texture kinetics of gradient XY enhancement yielded the best overall AUC=0:762±0:053, which was significantly higher than any feature enhancement representation (best AUC=0:696±0:050). Texture kinetic representations also outperformed their corresponding raw feature enhancement representations in 54.5% of the features compared, and performed significantly worse in only 13% of the comparisons. Non-invasive guidance of interventions in rectal cancers could therefore be enhanced through the use of texture kinetic features from DCE MRI, which may better characterize spatiotemporal differences between responders and non-responders on baseline imaging.\n
\n\n\n\n \n\n \n \n \n \n \n Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers.\n \n \n \n\n\n \n Selvam, A.; Antunes, J.; Bera, K.; Ofshteyn, A.; Brady, J.; Bingmer, K.; Friedman, K.; Stein, S.; Paspulati, R.; Purysko, A.; Kalady, M.; Madabhushi, A.; and Viswanath, S.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
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\n\n\n@misc{Selvam2020,\n abstract = {Tumor downstaging after neoadjuvant chemoradiation (CRT) in rectal cancer patients is typically assessed via Magnetic Resonance Imaging (MRI) in order to determine follow-up surgical interventions, but is associated with marked inter-reader variability and limited performance. While radiomic features have shown promise for evaluating chemoradiation response and tumor stage in rectal cancers, there is a need to determine how reproducible these features are across different MRI scanners and acquisitions. In this study, we evaluated radiomic feature reproducibility in terms of feature instability within a uniquely curated rue healthy rectum cohort in order to construct a stability-informed radiomic classifier for differentiating poorly from markedly down-staged rectal tumors after chemoradiation in a multi-site setting. We utilized a cohort of 156 patients, with (a) 74 MRIs visualizing the healthy rectum, (b) 52 post-CRT MRI scans in the discovery cohort, and (c) 30 post-CRT MRI scans in a second-site validation cohort; the latter 2 being from rectal cancer patients. 764 radiomic features were extracted from within the entire rectal wall on each MRI scan. Feature instability was used to quantify how reproducible each radiomic feature was between the discovery cohort and the healthy rectum cohort, using locations along the rectum that were spatially distinct from the treated tumor region. From the resulting tability-informed feature set, the most relevant features were identified to distinguish pathologic tumor stage groups in the discovery cohort via a QDA classifier with cross-validation to ensure robustness. The top 4 radiomic features were then evaluated in hold-out fashion on scans from the validation cohort. We found that utilizing a stability-informed radiomic model (which comprised features that were reproducible in 100% of all comparisons) was significantly more accurate in identifying pathological tumor stage regression in both discovery (AUC=0:66 ± 0:09) and validation (AUC=0.73) cohorts, compared to a basic radiomic model that used all extracted features (AUC=0:60 ± 0:07 in discovery, AUC=0.62 in validation). Evaluating feature instability with respect to healthy rectal tissue may thus enhance the performance of radiomic models in characterizing pathologic downstaging in rectal cancers, via MRI.},\n author = {A. Selvam and J.T. Antunes and K. Bera and A. Ofshteyn and J.T. Brady and K.E. Bingmer and K.A. Friedman and S.L. Stein and R.M. Paspulati and A.S. Purysko and M.F. Kalady and A. Madabhushi and S.E. Viswanath},\n doi = {10.1117/12.2549085},\n isbn = {9781510633957},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {mri,multi-site,radiomics,rectal cancer,reproducibility,stability},\n title = {Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers},\n volume = {11314},\n year = {2020},\n}\n\n\n Tumor downstaging after neoadjuvant chemoradiation (CRT) in rectal cancer patients is typically assessed via Magnetic Resonance Imaging (MRI) in order to determine follow-up surgical interventions, but is associated with marked inter-reader variability and limited performance. While radiomic features have shown promise for evaluating chemoradiation response and tumor stage in rectal cancers, there is a need to determine how reproducible these features are across different MRI scanners and acquisitions. In this study, we evaluated radiomic feature reproducibility in terms of feature instability within a uniquely curated rue healthy rectum cohort in order to construct a stability-informed radiomic classifier for differentiating poorly from markedly down-staged rectal tumors after chemoradiation in a multi-site setting. We utilized a cohort of 156 patients, with (a) 74 MRIs visualizing the healthy rectum, (b) 52 post-CRT MRI scans in the discovery cohort, and (c) 30 post-CRT MRI scans in a second-site validation cohort; the latter 2 being from rectal cancer patients. 764 radiomic features were extracted from within the entire rectal wall on each MRI scan. Feature instability was used to quantify how reproducible each radiomic feature was between the discovery cohort and the healthy rectum cohort, using locations along the rectum that were spatially distinct from the treated tumor region. From the resulting tability-informed feature set, the most relevant features were identified to distinguish pathologic tumor stage groups in the discovery cohort via a QDA classifier with cross-validation to ensure robustness. The top 4 radiomic features were then evaluated in hold-out fashion on scans from the validation cohort. We found that utilizing a stability-informed radiomic model (which comprised features that were reproducible in 100% of all comparisons) was significantly more accurate in identifying pathological tumor stage regression in both discovery (AUC=0:66 ± 0:09) and validation (AUC=0.73) cohorts, compared to a basic radiomic model that used all extracted features (AUC=0:60 ± 0:07 in discovery, AUC=0.62 in validation). Evaluating feature instability with respect to healthy rectal tissue may thus enhance the performance of radiomic models in characterizing pathologic downstaging in rectal cancers, via MRI.\n
\n\n\n\n \n\n \n \n \n \n \n Quality assessment of brain MRI scans using a dense neural network model and image metrics.\n \n \n \n\n\n \n Gupta, A.; Sadri, A.; Viswanath, S.; and Tiwari, P.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
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\n\n\n@misc{Gupta2020,\n abstract = {Structural MRI is the standard-of-care imaging modality for screening and diagnosis of most neurological conditions. However, the ability to reliably evaluate brain MRIs for disease characterization (whether by machines or experts) is often hampered by the presence of artifacts such as magnetic field inhomogeneity, aliasing, or patient motion; some of which may not be visually apparent. Reliable quality assessment of brain MRI scans would allow for excluding noisy acquisitions and reducing errors in subsequent downstream analyses. Since visual inspection is impractical for large volumes of data and subject to inter-observer variability, there is a need for accurate, automated Quality Assessment (QA) of MR images. Previous studies have investigated image quality metrics (IQMs) in order to quantify the effect of specific artifacts and quality degradation in an MR image. There has also been some recent work in developing machine learning models which use IQMs to enable robust QA across multiple sites. We build on this approach by leveraging a total of 64 IQMs (quantifying noise, artifacts, information, and general brain measurements) within a Dense Neural Network (DNN) model for QA of brain MRI scans using the publicly available multi-site ABIDE-I cohort (17 sites, 1102 subjects). In attempting to predict the “mean opinion score” of MR image quality (as assessed by expert radiologists), the DNN model yielded an accuracy of 87.3±2.1% in training (15 sites, 885 subjects) which generalized to a 79.5±1.9% accuracy in validation (2 sites, 246 subjects). This initial DNN model suggests the promise of deep learning to improve automated QA of MRI scans in large multi-site, multi-scanner cohorts.},\n author = {A. Gupta and A.R. Sadri and S.E. Viswanath and P. Tiwari},\n doi = {10.1117/12.2551348},\n isbn = {9781510633919},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {ABIDE,Deep learning,MRI,Quality assessment,Quality control},\n title = {Quality assessment of brain MRI scans using a dense neural network model and image metrics},\n volume = {11312},\n year = {2020},\n}\n\n\n Structural MRI is the standard-of-care imaging modality for screening and diagnosis of most neurological conditions. However, the ability to reliably evaluate brain MRIs for disease characterization (whether by machines or experts) is often hampered by the presence of artifacts such as magnetic field inhomogeneity, aliasing, or patient motion; some of which may not be visually apparent. Reliable quality assessment of brain MRI scans would allow for excluding noisy acquisitions and reducing errors in subsequent downstream analyses. Since visual inspection is impractical for large volumes of data and subject to inter-observer variability, there is a need for accurate, automated Quality Assessment (QA) of MR images. Previous studies have investigated image quality metrics (IQMs) in order to quantify the effect of specific artifacts and quality degradation in an MR image. There has also been some recent work in developing machine learning models which use IQMs to enable robust QA across multiple sites. We build on this approach by leveraging a total of 64 IQMs (quantifying noise, artifacts, information, and general brain measurements) within a Dense Neural Network (DNN) model for QA of brain MRI scans using the publicly available multi-site ABIDE-I cohort (17 sites, 1102 subjects). In attempting to predict the “mean opinion score” of MR image quality (as assessed by expert radiologists), the DNN model yielded an accuracy of 87.3±2.1% in training (15 sites, 885 subjects) which generalized to a 79.5±1.9% accuracy in validation (2 sites, 246 subjects). This initial DNN model suggests the promise of deep learning to improve automated QA of MRI scans in large multi-site, multi-scanner cohorts.\n
\n\n\n\n \n\n \n \n \n \n \n Radiomic texture and shape descriptors of the rectal environment on post-chemoradiation T2-weighted MRI are associated with pathologic tumor stage regression in rectal cancers: A retrospective, multi-institution study.\n \n \n \n\n\n \n Alvarez-Jimenez, C.; Antunes, J.; Talasila, N.; Bera, K.; Brady, J.; Gollamudi, J.; Marderstein, E.; Kalady, M.; Purysko, A.; Willis, J.; Stein, S.; Friedman, K.; Paspulati, R.; Delaney, C.; Romero, E.; Madabhushi, A.; and Viswanath, S.\n\n\n \n\n\n\n Cancers, 12. 2020.\n \n\n\n\n
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\n\n\n@article{,\n abstract = {(1) Background: The relatively poor expert restaging accuracy of MRI in rectal cancer after neoadjuvant chemoradiation may be due to the difficulties in visual assessment of residual tumor on post-treatment MRI. In order to capture underlying tissue alterations and morphologic changes in rectal structures occurring due to the treatment, we hypothesized that radiomics texture and shape descriptors of the rectal environment (e.g., wall, lumen) on post-chemoradiation T2-weighted (T2w) MRI may be associated with tumor regression after neoadjuvant chemoradiation therapy (nCRT). (2) Methods: A total of 94 rectal cancer patients were retrospectively identified from three collaborating institutions, for whom a 1.5 or 3T T2w MRI was available after nCRT and prior to surgical resection. The rectal wall and the lumen were annotated by an expert radiologist on all MRIs, based on which 191 texture descriptors and 198 shape descriptors were extracted for each patient. (3) Results: Top-ranked features associated with pathologic tumor-stage regression were identified via cross-validation on a discovery set (n = 52, 1 institution) and evaluated via discriminant analysis in hold-out validation (n = 42, 2 institutions). The best performing features for distinguishing low (ypT0-2) and high (ypT3–4) pathologic tumor stages after nCRT comprised directional gradient texture expression and morphologic shape differences in the entire rectal wall and lumen. Not only were these radiomic features found to be resilient to variations in magnetic field strength and expert segmentations, a quadratic discriminant model combining them yielded consistent performance across multiple institutions (hold-out AUC of 0.73). (4) Conclusions: Radiomic texture and shape descriptors of the rectal wall from post-treatment T2w MRIs may be associated with low and high pathologic tumor stage after neoadjuvant chemoradiation therapy and generalized across variations between scanners and institutions.},\n author = {C. Alvarez-Jimenez and J.T. Antunes and N. Talasila and K. Bera and J.T. Brady and J. Gollamudi and E.L. Marderstein and M.F. Kalady and A.S. Purysko and J.E. Willis and S.L. Stein and K.A. Friedman and R.M. Paspulati and C.P. Delaney and E.C. Romero and A. Madabhushi and S.E. Viswanath},\n doi = {10.3390/cancers12082027},\n issn = {20726694},\n issue = {8},\n journal = {Cancers},\n keywords = {Machine learning,Magnetic resonance imaging,Radiomics,Rectal cancer,Shape,Texture,Treatment response},\n title = {Radiomic texture and shape descriptors of the rectal environment on post-chemoradiation T2-weighted MRI are associated with pathologic tumor stage regression in rectal cancers: A retrospective, multi-institution study},\n volume = {12},\n year = {2020},\n}\n\n\n (1) Background: The relatively poor expert restaging accuracy of MRI in rectal cancer after neoadjuvant chemoradiation may be due to the difficulties in visual assessment of residual tumor on post-treatment MRI. In order to capture underlying tissue alterations and morphologic changes in rectal structures occurring due to the treatment, we hypothesized that radiomics texture and shape descriptors of the rectal environment (e.g., wall, lumen) on post-chemoradiation T2-weighted (T2w) MRI may be associated with tumor regression after neoadjuvant chemoradiation therapy (nCRT). (2) Methods: A total of 94 rectal cancer patients were retrospectively identified from three collaborating institutions, for whom a 1.5 or 3T T2w MRI was available after nCRT and prior to surgical resection. The rectal wall and the lumen were annotated by an expert radiologist on all MRIs, based on which 191 texture descriptors and 198 shape descriptors were extracted for each patient. (3) Results: Top-ranked features associated with pathologic tumor-stage regression were identified via cross-validation on a discovery set (n = 52, 1 institution) and evaluated via discriminant analysis in hold-out validation (n = 42, 2 institutions). The best performing features for distinguishing low (ypT0-2) and high (ypT3–4) pathologic tumor stages after nCRT comprised directional gradient texture expression and morphologic shape differences in the entire rectal wall and lumen. Not only were these radiomic features found to be resilient to variations in magnetic field strength and expert segmentations, a quadratic discriminant model combining them yielded consistent performance across multiple institutions (hold-out AUC of 0.73). (4) Conclusions: Radiomic texture and shape descriptors of the rectal wall from post-treatment T2w MRIs may be associated with low and high pathologic tumor stage after neoadjuvant chemoradiation therapy and generalized across variations between scanners and institutions.\n
\n\n\n\n \n\n \n \n \n \n \n Radiomic Features of Primary Rectal Cancers on Baseline T2 -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study.\n \n \n \n\n\n \n Antunes, J.; Ofshteyn, A.; Bera, K.; Wang, E.; Brady, J.; Willis, J.; Friedman, K.; Marderstein, E.; Kalady, M.; Stein, S.; Purysko, A.; Paspulati, R.; Gollamudi, J.; Madabhushi, A.; and Viswanath, S.\n\n\n \n\n\n\n Journal of Magnetic Resonance Imaging, 52. 2020.\n \n\n\n\n
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\n\n\n@article{Antunes2020,\n abstract = {Background: Twenty-five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers. Purpose: To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites. Study Type: Retrospective. Subjects: In all, 104 rectal cancer patients staged with MRI prior to long-course chemoradiation followed by proctectomy; curated from three institutions. Field Strength/Sequence: 1.5T–3.0T, axial higher resolution T<inf>2</inf>-weighted turbo spin echo sequence. Assessment: Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single-slice sections of rectal tumors on processed pretreatment T<inf>2</inf>-weighted MRI. Statistical Tests: Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross-validation). The top-selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity. Results: Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold-out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07–0.96). Data Conclusion: Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites. Level of Evidence: 3. Technical Efficacy Stage: 2.},\n author = {J.T. Antunes and A. Ofshteyn and K. Bera and E.Y. Wang and J.T. Brady and J.E. Willis and K.A. Friedman and E.L. Marderstein and M.F. Kalady and S.L. Stein and A.S. Purysko and R.M. Paspulati and J. Gollamudi and A. Madabhushi and S.E. Viswanath},\n doi = {10.1002/jmri.27140},\n issn = {15222586},\n issue = {5},\n journal = {Journal of Magnetic Resonance Imaging},\n keywords = {machine learning,pathologic complete response,radiomics,rectal cancer},\n title = {Radiomic Features of Primary Rectal Cancers on Baseline T<inf>2</inf>-Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study},\n volume = {52},\n year = {2020},\n}\n\n\n Background: Twenty-five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers. Purpose: To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites. Study Type: Retrospective. Subjects: In all, 104 rectal cancer patients staged with MRI prior to long-course chemoradiation followed by proctectomy; curated from three institutions. Field Strength/Sequence: 1.5T–3.0T, axial higher resolution T2 -weighted turbo spin echo sequence. Assessment: Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single-slice sections of rectal tumors on processed pretreatment T2 -weighted MRI. Statistical Tests: Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross-validation). The top-selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity. Results: Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold-out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07–0.96). Data Conclusion: Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites. Level of Evidence: 3. Technical Efficacy Stage: 2.\n
\n\n\n\n \n\n \n \n \n \n \n Technical Note: MRQy — An open-source tool for quality control of MR imaging data.\n \n \n \n\n\n \n Sadri, A.; Janowczyk, A.; Zhou, R.; Verma, R.; Beig, N.; Antunes, J.; Madabhushi, A.; Tiwari, P.; and Viswanath, S.\n\n\n \n\n\n\n Medical Physics, 47. 2020.\n \n\n\n\n
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\n\n\n@article{Sadri2020,\n abstract = {Purpose: There is an increasing availability of large imaging cohorts [such as through The Cancer Imaging Archive (TCIA)] for computational model development and imaging research. To ensure development of generalizable computerized models, there is a need to quickly determine relative quality differences in these cohorts, especially when considering MRI datasets which can exhibit wide variations in image appearance. The purpose of this study is to present a quantitative quality control tool, MRQy, to help interrogate MR imaging datasets for: (a) site- or scanner-specific variations in image resolution or image contrast, and (b) imaging artifacts such as noise or inhomogeneity; which need correction prior to model development. Methods: Unlike existing imaging quality control tools, MRQy has been generalized to work with images from any body region to efficiently extract a series of quality measures (e.g., noise ratios, variation metrics) and MR image metadata (e.g., voxel resolution and image dimensions). MRQy also offers a specialized HTML5-based front-end designed for real-time filtering and trend visualization of quality measures. Results: MRQy was used to evaluate (a) n = 133 brain MRIs from TCIA (7 sites) and (b) n = 104 rectal MRIs (3 local sites). MRQy measures revealed significant site-specific variations in both cohorts, indicating potential batch effects. Before processing, MRQy measures could be used to identify each of the seven sites within the TCIA cohort with 87.5%, 86.4%, 90%, 93%, 90%, 60%, and 92.9% accuracy and the three sites within the rectal cohort with 91%, 82.8%, and 88.9% accuracy using unsupervised clustering. After processing, none of the sites could be distinctively clustered via MRQy measures in either cohort; suggesting that batch effects had been largely accounted for. Marked differences in specific MRQy measures were also able to identify outlier MRI datasets that needed to be corrected for common acquisition artifacts. Conclusions: MRQy is designed to be a standalone, unsupervised tool that can be efficiently run on a standard desktop computer. It has been made freely accessible and open-source at http://github.com/ccipd/MRQy for community use and feedback.},\n author = {A.R. Sadri and A.R. Janowczyk and R. Zhou and R. Verma and N.G. Beig and J.T. Antunes and A. Madabhushi and P. Tiwari and S.E. Viswanath},\n doi = {10.1002/mp.14593},\n issn = {24734209},\n issue = {12},\n journal = {Medical Physics},\n keywords = {MRI,acquisition artifacts,batch effects,imaging variations,noise,quality control},\n title = {Technical Note: MRQy — An open-source tool for quality control of MR imaging data},\n volume = {47},\n year = {2020},\n}\n\n\n Purpose: There is an increasing availability of large imaging cohorts [such as through The Cancer Imaging Archive (TCIA)] for computational model development and imaging research. To ensure development of generalizable computerized models, there is a need to quickly determine relative quality differences in these cohorts, especially when considering MRI datasets which can exhibit wide variations in image appearance. The purpose of this study is to present a quantitative quality control tool, MRQy, to help interrogate MR imaging datasets for: (a) site- or scanner-specific variations in image resolution or image contrast, and (b) imaging artifacts such as noise or inhomogeneity; which need correction prior to model development. Methods: Unlike existing imaging quality control tools, MRQy has been generalized to work with images from any body region to efficiently extract a series of quality measures (e.g., noise ratios, variation metrics) and MR image metadata (e.g., voxel resolution and image dimensions). MRQy also offers a specialized HTML5-based front-end designed for real-time filtering and trend visualization of quality measures. Results: MRQy was used to evaluate (a) n = 133 brain MRIs from TCIA (7 sites) and (b) n = 104 rectal MRIs (3 local sites). MRQy measures revealed significant site-specific variations in both cohorts, indicating potential batch effects. Before processing, MRQy measures could be used to identify each of the seven sites within the TCIA cohort with 87.5%, 86.4%, 90%, 93%, 90%, 60%, and 92.9% accuracy and the three sites within the rectal cohort with 91%, 82.8%, and 88.9% accuracy using unsupervised clustering. After processing, none of the sites could be distinctively clustered via MRQy measures in either cohort; suggesting that batch effects had been largely accounted for. Marked differences in specific MRQy measures were also able to identify outlier MRI datasets that needed to be corrected for common acquisition artifacts. Conclusions: MRQy is designed to be a standalone, unsupervised tool that can be efficiently run on a standard desktop computer. It has been made freely accessible and open-source at http://github.com/ccipd/MRQy for community use and feedback.\n
\n\n\n\n \n\n \n \n \n \n \n Identifying cross-scale associations between radiomic and pathomic signatures of non-small cell lung cancer subtypes: Preliminary results.\n \n \n \n\n\n \n Alvarez-Jimenez, C.; Sandino, A.; Prasanna, P.; Gupta, A.; Viswanath, S.; and Romero, E.\n\n\n \n\n\n\n Cancers, 12. 2020.\n \n\n\n\n
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\n\n\n@article{,\n abstract = {(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.},\n author = {C. Alvarez-Jimenez and A.A. Sandino and P. Prasanna and A. Gupta and S.E. Viswanath and E.C. Romero},\n doi = {10.3390/cancers12123663},\n issn = {20726694},\n issue = {12},\n journal = {Cancers},\n keywords = {Association,CT,Cell density,Correlation,Digital pathology,Lung cancer,Pathomics,Radiomics},\n title = {Identifying cross-scale associations between radiomic and pathomic signatures of non-small cell lung cancer subtypes: Preliminary results},\n volume = {12},\n year = {2020},\n}\n\n\n (1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.\n
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\n \n \n\n \n \n \n \n \n Integrating radiomic features from T2-weighted and contrast-enhanced MRI to evaluate pathologic rectal tumor regression after chemoradiation.\n \n \n \n\n\n \n Nanda, S.; Antunes, J.; Selvam, A.; Bera, K.; Brady, J.; Gollamudi, J.; Friedman, K.; Willis, J.; Delaney, C.; Paspulati, R.; Madabhushi, A.; and Viswanath, S.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
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\n\n\n@misc{Nanda2019,\n abstract = {A major clinical challenge in rectal cancer currently is non-invasive identification of tumor regression to standard-of-care neoadjuvant chemoradiation (CRT). Multi-parametric MRI is routinely acquired after CRT, but expert radiologists find it highly challenging to assess the degree of tumor regression on both T2-weighted (T2w) and Gadolinium contrast-enhanced (CE) MRI; resulting in poor agreement with gold-standard pathologic evaluation. In this study, we present initial results for integrating quantitative image appearance (radiomic) features from post-CRT T2w and CE MRI towards in vivo assessment of pathologic rectal tumor response to chemoradiation. 29 rectal cancer patients with post-CRT multi-parametric 3 T MRI (with T2w, initial and delayed CE phases) were included in this study. Through spatial co-registration, the treated region of the rectal wall was identified and annotated on T2w and all CE phases (as well as correcting for motion artifacts in CE MRI). 165 radiomic features (including Haralick, Gabor, Laws, Sobel/Kirsch) were separately extracted from each of T2w and 2 CE phases; within the entire rectal wall. The top 2 response-associated radiomic features for each of (a) T2w, (b) 2 CE phases, (c) combined T2w+CE phases were identified via feature selection and evaluated in a leave-one-patient-out cross validation setting. Integrating T2w and CE radiomic features was found to be markedly more accurate (AUC=0.93) for assessing post-CRT pathologic tumor stage, compared to T2w radiomic features (AUC=0.80) and CE radiomic features (AUC=0.63) individually. Top-ranked features captured heterogeneity of gradient responses on T2w MRI and macro-scale Gabor wavelet responses of contrast enhancement on CE MRI. Combining radiomic features from post-CRT T2w and CE MRI may hence enable more comprehensive evaluation of response to neoadjuvant therapy in rectal cancers, which can be used to better guide follow-up interventions.},\n author = {S. Nanda and J.T. Antunes and A. Selvam and K. Bera and J.T. Brady and J. Gollamudi and K.A. Friedman and J.E. Willis and C.P. Delaney and R.M. Paspulati and A. Madabhushi and S.E. Viswanath},\n doi = {10.1117/12.2513945},\n isbn = {9781510625495},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Contrast enhancement,MRI,Multi-parametric,Radiomics,Rectal cancer,Response assessment,T2w,Tumor regression},\n title = {Integrating radiomic features from T2-weighted and contrast-enhanced MRI to evaluate pathologic rectal tumor regression after chemoradiation},\n volume = {10951},\n year = {2019},\n}\n\n\n A major clinical challenge in rectal cancer currently is non-invasive identification of tumor regression to standard-of-care neoadjuvant chemoradiation (CRT). Multi-parametric MRI is routinely acquired after CRT, but expert radiologists find it highly challenging to assess the degree of tumor regression on both T2-weighted (T2w) and Gadolinium contrast-enhanced (CE) MRI; resulting in poor agreement with gold-standard pathologic evaluation. In this study, we present initial results for integrating quantitative image appearance (radiomic) features from post-CRT T2w and CE MRI towards in vivo assessment of pathologic rectal tumor response to chemoradiation. 29 rectal cancer patients with post-CRT multi-parametric 3 T MRI (with T2w, initial and delayed CE phases) were included in this study. Through spatial co-registration, the treated region of the rectal wall was identified and annotated on T2w and all CE phases (as well as correcting for motion artifacts in CE MRI). 165 radiomic features (including Haralick, Gabor, Laws, Sobel/Kirsch) were separately extracted from each of T2w and 2 CE phases; within the entire rectal wall. The top 2 response-associated radiomic features for each of (a) T2w, (b) 2 CE phases, (c) combined T2w+CE phases were identified via feature selection and evaluated in a leave-one-patient-out cross validation setting. Integrating T2w and CE radiomic features was found to be markedly more accurate (AUC=0.93) for assessing post-CRT pathologic tumor stage, compared to T2w radiomic features (AUC=0.80) and CE radiomic features (AUC=0.63) individually. Top-ranked features captured heterogeneity of gradient responses on T2w MRI and macro-scale Gabor wavelet responses of contrast enhancement on CE MRI. Combining radiomic features from post-CRT T2w and CE MRI may hence enable more comprehensive evaluation of response to neoadjuvant therapy in rectal cancers, which can be used to better guide follow-up interventions.\n
\n\n\n\n \n\n \n \n \n \n \n Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation.\n \n \n \n\n\n \n Yim, M.; Wei, Z.; Antunes, J.; Sehgal, N.; Bera, K.; Brady, J.; Friedman, K.; Willis, J.; Purysko, A.; Paspulati, R.; Madabhushi, A.; and Viswanath, S.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
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\n\n\n@misc{Yim2019,\n abstract = {Evaluating tumor regression of rectal cancers via MRI after standard-of-care chemoradiation therapy (CRT) remains highly challenging for radiologists. While the tumor region-of-interest (ROI) on post-CRT rectal MRI is difficult to localize, an underexplored region is the perirectal fat (surrounding tumor and rectum) where residual cancer cells and positive lymph nodes are known to be present. Recent studies have shown that physiologic environments surrounding tumor regions may provide complementary information that is predictive of response to CRT and patient survival. We present initial results of characterizing perirectal fat regions on MRI via radiomics, towards capturing sub-visual details related to rectal tumor or nodal response to CRT. A total of 37 rectal cancer patients for whom MRIs as well as pathologic tumor staging were available post-CRT were included in this study. Region-wise radiomic features were extracted from expert annotated perirectal fat regions and a 2-stage feature selection was employed to identify the most relevant features. Radiomic entropy of perirectal fat was found to be over-expressed in patients with poor tumor or nodal response post-CRT, albeit with different spatial distributions. In a leave-one-patient-out cross validation setting, a quadratic discriminant analysis (QDA) classifier trained on top radiomic features from the perirectal fat achieved AUCs of 0.77 (for differentiating incomplete vs marked tumor regression) and 0.75 (for differentiating lymph node positive from negative patients). By comparison, perirectal fat intensities achieved significantly poorer AUCs in both tasks. Our results indicate perirectal fat on post-CRT MRI may be highly relevant for evaluating CRT response and informing follow-on interventions in rectal cancers.},\n author = {M.C. Yim and Z. Wei and J.T. Antunes and N.K. Sehgal and K. Bera and J.T. Brady and K.A. Friedman and J.E. Willis and A.S. Purysko and R.M. Paspulati and A. Madabhushi and S.E. Viswanath},\n doi = {10.1117/12.2512612},\n isbn = {9781510625495},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Fat,MRI,Node metastasis,Radiomics,Rectal cancer,Tumor regression},\n title = {Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation},\n volume = {10951},\n year = {2019},\n}\n\n\n Evaluating tumor regression of rectal cancers via MRI after standard-of-care chemoradiation therapy (CRT) remains highly challenging for radiologists. While the tumor region-of-interest (ROI) on post-CRT rectal MRI is difficult to localize, an underexplored region is the perirectal fat (surrounding tumor and rectum) where residual cancer cells and positive lymph nodes are known to be present. Recent studies have shown that physiologic environments surrounding tumor regions may provide complementary information that is predictive of response to CRT and patient survival. We present initial results of characterizing perirectal fat regions on MRI via radiomics, towards capturing sub-visual details related to rectal tumor or nodal response to CRT. A total of 37 rectal cancer patients for whom MRIs as well as pathologic tumor staging were available post-CRT were included in this study. Region-wise radiomic features were extracted from expert annotated perirectal fat regions and a 2-stage feature selection was employed to identify the most relevant features. Radiomic entropy of perirectal fat was found to be over-expressed in patients with poor tumor or nodal response post-CRT, albeit with different spatial distributions. In a leave-one-patient-out cross validation setting, a quadratic discriminant analysis (QDA) classifier trained on top radiomic features from the perirectal fat achieved AUCs of 0.77 (for differentiating incomplete vs marked tumor regression) and 0.75 (for differentiating lymph node positive from negative patients). By comparison, perirectal fat intensities achieved significantly poorer AUCs in both tasks. Our results indicate perirectal fat on post-CRT MRI may be highly relevant for evaluating CRT response and informing follow-on interventions in rectal cancers.\n
\n\n\n\n \n\n \n \n \n \n \n Region-specific fully convolutional networks for segmentation of the rectal wall on post-chemoradiation T2w MRI.\n \n \n \n\n\n \n DeSilvio, T.; Antunes, J.; Chirra, P.; Bera, K.; Gollamudi, J.; Paspulati, R.; Delaney, C.; and Viswanath, S.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
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\n\n\n@misc{DeSilvio2019,\n abstract = {Detailed localization of the rectal wall after chemoradiation on standard-of-care post-chemoradiation (CRT) MRIs could enable more targeted follow-up interventions, but it is a challenging and laborious task for radiologists. This may be because the primary tumor site (i.e. rimary» wall) and the remaining djacent» wall areas depict visually overlapping intensity characteristics as a result of chemoradiation-induced noise and treatment effects. In this study, we present initial results for developing and optimizing fully convolutional networks (FCNs) to automatically segment the rectal wall on post-CRT MRIs. Our cohort comprised 50 post-CRT, T2-weighted MRIs from rectal cancer patients with expert annotations of the entire length of the rectal wall (with separate indications for extent of primary wall as well as adjacent wall). The FCN framework was designed to provide a pixel-wise segmentation of the rectal wall while utilizing the original T2w intensity images, and was tested on 20% of the cohort that was held-out from training. Our results showed that (a) the best-performing FCN for segmenting primary wall areas utilized a training set comprising primary wall sections alone (median DSC = 0.71), while (b) optimal segmentations of adjacent wall areas were achieved by an FCN trained on both primary and adjacent wall sections (median DSC = 0.68). Notably, the primary wall FCN performed poorly when applied to adjacent wall and vice versa; perhaps indicating that fundamental physiological differences exist between these wall areas that must be accounted for within automated CN segmentation approaches. FCNs may hence have to be optimized on a region-specific basis to obtain detailed, accurate delineations of the entire rectal wall on post-CRT T2w MRI, towards more targeted excision surgery and adjuvant therapy.},\n author = {T. DeSilvio and J.T. Antunes and P.V. Chirra and K. Bera and J. Gollamudi and R.M. Paspulati and C.P. Delaney and S.E. Viswanath},\n doi = {10.1117/12.2513055},\n isbn = {9781510625495},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Deep learning,MRI,Rectal cancer,Segmentation},\n title = {Region-specific fully convolutional networks for segmentation of the rectal wall on post-chemoradiation T2w MRI},\n volume = {10951},\n year = {2019},\n}\n\n\n Detailed localization of the rectal wall after chemoradiation on standard-of-care post-chemoradiation (CRT) MRIs could enable more targeted follow-up interventions, but it is a challenging and laborious task for radiologists. This may be because the primary tumor site (i.e. rimary» wall) and the remaining djacent» wall areas depict visually overlapping intensity characteristics as a result of chemoradiation-induced noise and treatment effects. In this study, we present initial results for developing and optimizing fully convolutional networks (FCNs) to automatically segment the rectal wall on post-CRT MRIs. Our cohort comprised 50 post-CRT, T2-weighted MRIs from rectal cancer patients with expert annotations of the entire length of the rectal wall (with separate indications for extent of primary wall as well as adjacent wall). The FCN framework was designed to provide a pixel-wise segmentation of the rectal wall while utilizing the original T2w intensity images, and was tested on 20% of the cohort that was held-out from training. Our results showed that (a) the best-performing FCN for segmenting primary wall areas utilized a training set comprising primary wall sections alone (median DSC = 0.71), while (b) optimal segmentations of adjacent wall areas were achieved by an FCN trained on both primary and adjacent wall sections (median DSC = 0.68). Notably, the primary wall FCN performed poorly when applied to adjacent wall and vice versa; perhaps indicating that fundamental physiological differences exist between these wall areas that must be accounted for within automated CN segmentation approaches. FCNs may hence have to be optimized on a region-specific basis to obtain detailed, accurate delineations of the entire rectal wall on post-CRT T2w MRI, towards more targeted excision surgery and adjuvant therapy.\n
\n\n\n\n \n\n \n \n \n \n \n Structural rectal atlas deformation (stRAD) features for characterizing intra- and peri-wall chemoradiation response on MRI.\n \n \n \n\n\n \n Antunes, J.; Wei, Z.; Alvarez-Jimenez, C.; Romero, E.; Ismail, M.; Madabhushi, A.; Tiwari, P.; and Viswanath, S.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
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\n\n\n@misc{Antunes2019,\n abstract = {Radiomic features which quantify morphologic texture and shape of tumor regions on imaging have found wide success in characterizing treatment response in vivo. A more detailed interrogation of intra- and peri-tumoral regions for response-related cues could be achieved by capturing subtle structural deformations that occur due to tumor shrinkage or growth. In this work, we present a novel suite of STructural Rectal Atlas Deformation (StRAD) features to quantify tumor-related deformations in rectal cancers via a cohort of 139 patient MRIs. In flexible non-rigid organs such as the rectum, inter-patient differences complicate evaluation of tumor-related deformations that may occur within the rectal wall or in the peri-rectal environment; necessitating construction of a canonical rectal imaging atlas. Using 63 pelvic MRIs where healthy rectums could be clearly visualized, we built the first structural atlas for the healthy rectal wall. This atlas was used to compute structural deformations within and around locations in the rectal wall of patients where tumor was present, resulting in intra- and peri-wall StRAD descriptors. We evaluated the efficacy of our StRAD features in 2 different tasks: (a) predicting which rectal tumors will or will not respond to therapy via baseline MRIs (n = 42), and (b) identifying which rectal tumors were exhibiting regression on post-chemoradiation MRIs (n = 34). Using a linear discriminant analysis classifier in a three-fold cross-validation scheme, we found that intra-wall deformations were significantly lower for responders to chemoradiation; both on baseline MRIs (AUC = 0.73±0.05) as well as on post-therapy MRIs (AUC = 0.87±0.03). By comparison, radiomic texture features for both intra- and peri-wall locations yielded significantly worse classification performance in both tasks.},\n author = {J.T. Antunes and Z. Wei and C. Alvarez-Jimenez and E.C. Romero and M. Ismail and A. Madabhushi and P. Tiwari and S.E. Viswanath},\n doi = {10.1007/978-3-030-32251-9_67},\n isbn = {9783030322502},\n issn = {16113349},\n journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n title = {Structural rectal atlas deformation (stRAD) features for characterizing intra- and peri-wall chemoradiation response on MRI},\n volume = {11767 LNCS},\n year = {2019},\n}\n\n\n Radiomic features which quantify morphologic texture and shape of tumor regions on imaging have found wide success in characterizing treatment response in vivo. A more detailed interrogation of intra- and peri-tumoral regions for response-related cues could be achieved by capturing subtle structural deformations that occur due to tumor shrinkage or growth. In this work, we present a novel suite of STructural Rectal Atlas Deformation (StRAD) features to quantify tumor-related deformations in rectal cancers via a cohort of 139 patient MRIs. In flexible non-rigid organs such as the rectum, inter-patient differences complicate evaluation of tumor-related deformations that may occur within the rectal wall or in the peri-rectal environment; necessitating construction of a canonical rectal imaging atlas. Using 63 pelvic MRIs where healthy rectums could be clearly visualized, we built the first structural atlas for the healthy rectal wall. This atlas was used to compute structural deformations within and around locations in the rectal wall of patients where tumor was present, resulting in intra- and peri-wall StRAD descriptors. We evaluated the efficacy of our StRAD features in 2 different tasks: (a) predicting which rectal tumors will or will not respond to therapy via baseline MRIs (n = 42), and (b) identifying which rectal tumors were exhibiting regression on post-chemoradiation MRIs (n = 34). Using a linear discriminant analysis classifier in a three-fold cross-validation scheme, we found that intra-wall deformations were significantly lower for responders to chemoradiation; both on baseline MRIs (AUC = 0.73±0.05) as well as on post-therapy MRIs (AUC = 0.87±0.03). By comparison, radiomic texture features for both intra- and peri-wall locations yielded significantly worse classification performance in both tasks.\n
\n\n\n\n \n\n \n \n \n \n \n Radiomics in genitourinary cancers: Prostate cancer.\n \n \n \n\n\n \n Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
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\n\n\n@misc{Viswanath2019,\n abstract = {With increasing use of MR imaging for the prostate, a variety of radiomic approaches have been proposed for machine learning-based disease diagnosis, prognosis, and response prediction. Popular radiomic features quantify localized intensity and filter responses to capture variations in signal intensity values within a region of interest. However, T2w MR intensity values do not exhibit tissue-specific meaning between patients, scanners, and institutions, likely impacting the robustness of MRI-based radiomic features. We present a case study on correcting inherent intensity drift on the original T2w signal intensity values as well as associated radiomic features, in a multi-site setting. This study utilized 147 T2-weighted prostate MRI datasets curated from across 4 different sites. 131 radiomic texture features were extracted from within expert-annotated tumor and non-tumor regions. A unique measure of feature instability was utilized to quantify cross-site reproducibility, between the pre- and post-standardization MRI datasets. Standardization resulted in tumor and non-tumor region intensities becoming reproducible in over 99% of all cross-site comparisons, indicating an improved numeric consistency in standardized T2w intensities across sites. Only 8% of all 131 radiomic features exhibited worse cross-site reproducibility after standardization, which were localized complex co-occurrence and macro-scale wavelet features within non-tumor regions alone. Intensity standardization may be a critical post-processing step for more reproducible radiomic characterization of prostate T2w MRI data, across multiple sites.},\n author = {S.E. Viswanath and A. Madabhushi},\n doi = {10.1201/9781351208277-18},\n isbn = {9781351208260},\n journal = {Radiomics and Radiogenomics: Technical Basis and Clinical Applications},\n title = {Radiomics in genitourinary cancers: Prostate cancer},\n year = {2019},\n}\n\n\n With increasing use of MR imaging for the prostate, a variety of radiomic approaches have been proposed for machine learning-based disease diagnosis, prognosis, and response prediction. Popular radiomic features quantify localized intensity and filter responses to capture variations in signal intensity values within a region of interest. However, T2w MR intensity values do not exhibit tissue-specific meaning between patients, scanners, and institutions, likely impacting the robustness of MRI-based radiomic features. We present a case study on correcting inherent intensity drift on the original T2w signal intensity values as well as associated radiomic features, in a multi-site setting. This study utilized 147 T2-weighted prostate MRI datasets curated from across 4 different sites. 131 radiomic texture features were extracted from within expert-annotated tumor and non-tumor regions. A unique measure of feature instability was utilized to quantify cross-site reproducibility, between the pre- and post-standardization MRI datasets. Standardization resulted in tumor and non-tumor region intensities becoming reproducible in over 99% of all cross-site comparisons, indicating an improved numeric consistency in standardized T2w intensities across sites. Only 8% of all 131 radiomic features exhibited worse cross-site reproducibility after standardization, which were localized complex co-occurrence and macro-scale wavelet features within non-tumor regions alone. Intensity standardization may be a critical post-processing step for more reproducible radiomic characterization of prostate T2w MRI data, across multiple sites.\n
\n\n\n\n \n\n \n \n \n \n \n Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: A multi-site study.\n \n \n \n\n\n \n Viswanath, S.; Chirra, P.; Yim, M.; Rofsky, N.; Purysko, A.; Rosen, M.; Bloch, B.; and Madabhushi, A.\n\n\n \n\n\n\n BMC Medical Imaging, 19. 2019.\n \n\n\n\n
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\n\n\n@article{Viswanath2019,\n abstract = {Background: For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. Methods: Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve. Results: The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods. Conclusions: Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.},\n author = {S.E. Viswanath and P.V. Chirra and M.C. Yim and N.M. Rofsky and A.S. Purysko and M.A. Rosen and B.N. Bloch and A. Madabhushi},\n doi = {10.1186/s12880-019-0308-6},\n issn = {14712342},\n issue = {1},\n journal = {BMC Medical Imaging},\n keywords = {Classifiers,Comparison,MRI,Prostate cancer,Radiomics},\n title = {Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: A multi-site study},\n volume = {19},\n year = {2019},\n}\n\n\n Background: For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. Methods: Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve. Results: The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods. Conclusions: Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.\n
\n\n\n\n \n\n \n \n \n \n \n Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.\n \n \n \n\n\n \n Chirra, P.; Leo, P.; Yim, M.; Bloch, B.; Rastinehad, A.; Purysko, A.; Rosen, M.; Madabhushi, A.; and Viswanath, S.\n\n\n \n\n\n\n Journal of Medical Imaging, 6. 2019.\n \n\n\n\n
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\n\n\n@article{Chirra2019,\n abstract = {Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1-or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈0.8). By contrast, a majority of Laws features are highly variable across sites (reproducible in <75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies (<0.6), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.},\n author = {P.V. Chirra and P. Leo and M.C. Yim and B.N. Bloch and A.R. Rastinehad and A.S. Purysko and M.A. Rosen and A. Madabhushi and S.E. Viswanath},\n doi = {10.1117/1.JMI.6.2.024502},\n issn = {23294310},\n issue = {2},\n journal = {Journal of Medical Imaging},\n keywords = {discriminability,feature analysis,magnetic resonance imaging,multisite,prostate,radiomics,reproducibility,stability},\n title = {Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI},\n volume = {6},\n year = {2019},\n}\n\n\n Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1-or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈0.8). By contrast, a majority of Laws features are highly variable across sites (reproducible in <75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies (<0.6), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.\n
\n\n\n\n \n\n \n \n \n \n \n Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI.\n \n \n \n\n\n \n Alvarez-Jimenez, C.; Barrera, C.; Múnera, N.; Viswanath, S.; and Romero, E.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
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\n\n\n@misc{,\n abstract = {Prostate cancer (PCa) diagnosis is established by pathological examination via biopsies, which are associated with significant complications and false negatives. Using MRIs to identify locations with high probability of containing cancer could instead be used to guide the biopsy procedure. The present investigation aims to identify target regions within different prostatic zones on MRI with high probability of being cancerous for assisting in the decision of where and how to perform biopsy. Our approach involved extracting multi-scale texture features for capturing local patterns to distinguish cancer and healthy tissue in different T2W-MRI prostate zones. Three different classification models were fed by the proposed strategy, namely support vector machine (SVM), Adaboost, and Random Forest. SVM with a linear kernel showed the best classification performance, with AUC scores of 0.91 in the anterior fibromuscular stroma area, 0.85 in the peripheral zone, and 0.87 when classification is performed independently of the prostate zone. The proposed method demonstrated that discriminant multi-scale texture features can accurately identify regions of prostate cancer in a zone-specific fashion, via MRI.},\n author = {C. Alvarez-Jimenez and C.R. Barrera and N. Múnera and S.E. Viswanath and E.C. Romero},\n doi = {10.1109/EMBC.2019.8856927},\n isbn = {9781538613115},\n issn = {1557170X},\n journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},\n title = {Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI},\n year = {2019},\n}\n\n\n Prostate cancer (PCa) diagnosis is established by pathological examination via biopsies, which are associated with significant complications and false negatives. Using MRIs to identify locations with high probability of containing cancer could instead be used to guide the biopsy procedure. The present investigation aims to identify target regions within different prostatic zones on MRI with high probability of being cancerous for assisting in the decision of where and how to perform biopsy. Our approach involved extracting multi-scale texture features for capturing local patterns to distinguish cancer and healthy tissue in different T2W-MRI prostate zones. Three different classification models were fed by the proposed strategy, namely support vector machine (SVM), Adaboost, and Random Forest. SVM with a linear kernel showed the best classification performance, with AUC scores of 0.91 in the anterior fibromuscular stroma area, 0.85 in the peripheral zone, and 0.87 when classification is performed independently of the prostate zone. The proposed method demonstrated that discriminant multi-scale texture features can accurately identify regions of prostate cancer in a zone-specific fashion, via MRI.\n
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\n \n \n\n \n \n \n \n \n Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI.\n \n \n \n\n\n \n Chirra, P.; Leo, P.; Yim, M.; Bloch, B.; Rastinehad, A.; Purysko, A.; Rosen, M.; Madabhushi, A.; and Viswanath, S.\n\n\n \n\n\n\n 2018.\n \n\n\n\n
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\n\n\n@misc{Chirra2018,\n abstract = {The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.},\n author = {P.V. Chirra and P. Leo and M.C. Yim and B.N. Bloch and A.R. Rastinehad and A.S. Purysko and M.A. Rosen and A. Madabhushi and S.E. Viswanath},\n doi = {10.1117/12.2293992},\n isbn = {9781510616394},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {MRI,Radiomics,feature analysis,multi-site,prostate,reproducibility,stability,variance},\n title = {Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI},\n volume = {10575},\n year = {2018},\n}\n\n\n The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.\n
\n\n\n\n \n\n \n \n \n \n \n Automated segmentation and radiomic characterization of visceral fat on bowel MRIs for Crohn's disease.\n \n \n \n\n\n \n Barbur, I.; Kurowski, J.; Bera, K.; Thawani, R.; Achkar, J.; Fiocchi, C.; Kay, M.; Gupta, R.; and Viswanath, S.\n\n\n \n\n\n\n 2018.\n \n\n\n\n
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\n\n\n@misc{Barbur2018,\n abstract = {Crohn's Disease is a relapsing and remitting disease involving chronic intestinal inflammation that is often characterized by hypertrophy of visceral adipose tissue (VAT). While an increased ratio of VAT to subcutaneous fat (SQF) has previously been identified as a predictor of worse outcomes in Crohn's Disease, bowel-proximal fat regions have also been hypothesized to play a role in inflammatory response. However, there has been no detailed study of VAT and SQF regions on MRI to determine their potential utility in assessing Crohn's Disease severity or guiding therapy. In this paper we present a fully-automated algorithm to segment and quantitatively characterize VAT and SQF via routinely acquired diagnostic bowel MRIs. Our automated segmentation scheme for VAT and SQF regions involved a combination of morphological processing and connected component analysis, and demonstrated DICE overlap scores of 0.86±0.05 and 0.91±0.04 respectively, when compared against expert annotations. Additionally, VAT regions proximal to the bowel wall (on diagnostic bowel MRIs) demonstrated a statistically significantly, higher expression of four unique radiomic features in pediatric patients with moderately active Crohn's Disease. These features were also able to accurately cluster patients who required aggressive biologic therapy within a year of diagnosis from those who did not, with 87.5% accuracy. Our findings indicate that quantitative radiomic characterization of visceral fat regions on bowel MRIs may be highly relevant for guiding therapeutic interventions in Crohn's Disease.},\n author = {I. Barbur and J.A. Kurowski and K. Bera and R. Thawani and J.P. Achkar and C. Fiocchi and M.H. Kay and R. Gupta and S.E. Viswanath},\n doi = {10.1117/12.2293533},\n isbn = {9781510616417},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Crohn's Disease,MRI,Radiomics,Segmentation,Small bowel,Visceral adipose tissue},\n title = {Automated segmentation and radiomic characterization of visceral fat on bowel MRIs for Crohn's disease},\n volume = {10576},\n year = {2018},\n}\n\n\n Crohn's Disease is a relapsing and remitting disease involving chronic intestinal inflammation that is often characterized by hypertrophy of visceral adipose tissue (VAT). While an increased ratio of VAT to subcutaneous fat (SQF) has previously been identified as a predictor of worse outcomes in Crohn's Disease, bowel-proximal fat regions have also been hypothesized to play a role in inflammatory response. However, there has been no detailed study of VAT and SQF regions on MRI to determine their potential utility in assessing Crohn's Disease severity or guiding therapy. In this paper we present a fully-automated algorithm to segment and quantitatively characterize VAT and SQF via routinely acquired diagnostic bowel MRIs. Our automated segmentation scheme for VAT and SQF regions involved a combination of morphological processing and connected component analysis, and demonstrated DICE overlap scores of 0.86±0.05 and 0.91±0.04 respectively, when compared against expert annotations. Additionally, VAT regions proximal to the bowel wall (on diagnostic bowel MRIs) demonstrated a statistically significantly, higher expression of four unique radiomic features in pediatric patients with moderately active Crohn's Disease. These features were also able to accurately cluster patients who required aggressive biologic therapy within a year of diagnosis from those who did not, with 87.5% accuracy. Our findings indicate that quantitative radiomic characterization of visceral fat regions on bowel MRIs may be highly relevant for guiding therapeutic interventions in Crohn's Disease.\n
\n\n\n\n \n\n \n \n \n \n \n A review of machine learning in obesity.\n \n \n \n\n\n \n DeGregory, K.; Kuiper, P.; DeSilvio, T.; Pleuss, J.; Miller, R.; Roginski, J.; Fisher, C.; Harness, D.; Viswanath, S.; Heymsfield, S.; Dungan, I.; and Thomas, D.\n\n\n \n\n\n\n Obesity Reviews, 19. 2018.\n \n\n\n\n
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\n\n\n@article{DeGregory2018,\n abstract = {Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.},\n author = {K.W. DeGregory and P.K. Kuiper and T. DeSilvio and J.D. Pleuss and R. Miller and J.W. Roginski and C.B. Fisher and D. Harness and S.E. Viswanath and S.B. Heymsfield and I. Dungan and D.M. Thomas},\n doi = {10.1111/obr.12667},\n issn = {1467789X},\n issue = {5},\n journal = {Obesity Reviews},\n keywords = {Deep learning,National Health and Nutrition Examination Survey,machine learning,topological data analysis},\n title = {A review of machine learning in obesity},\n volume = {19},\n year = {2018},\n}\n\n\n Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.\n
\n\n\n\n \n\n \n \n \n \n \n Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings.\n \n \n \n\n\n \n Antunes, J.; Viswanath, S.; Brady, J.; Crawshaw, B.; Ros, P.; Steele, S.; Delaney, C.; Paspulati, R.; Willis, J.; and Madabhushi, A.\n\n\n \n\n\n\n Academic Radiology, 25. 2018.\n \n\n\n\n
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\n\n\n@article{Antunes2018,\n abstract = {Rationale and Objectives: The objective of this study was to develop and quantitatively evaluate a radiology-pathology fusion method for spatially mapping tissue regions corresponding to different chemoradiation therapy-related effects from surgically excised whole-mount rectal cancer histopathology onto preoperative magnetic resonance imaging (MRI). Materials and Methods: This study included six subjects with rectal cancer treated with chemoradiation therapy who were then imaged with a 3-T T2-weighted MRI sequence, before undergoing mesorectal excision surgery. Excised rectal specimens were sectioned, stained, and digitized as two-dimensional (2D) whole-mount slides. Annotations of residual disease, ulceration, fibrosis, muscularis propria, mucosa, fat, inflammation, and pools of mucin were made by an expert pathologist on digitized slide images. An expert radiologist and pathologist jointly established corresponding 2D sections between MRI and pathology images, as well as identified a total of 10 corresponding landmarks per case (based on visually similar structures) on both modalities (five for driving registration and five for evaluating alignment). We spatially fused the in vivo MRI and ex vivo pathology images using landmark-based registration. This allowed us to spatially map detailed annotations from 2D pathology slides onto corresponding 2D MRI sections. Results: Quantitative assessment of coregistered pathology and MRI sections revealed excellent structural alignment, with an overall deviation of 1.50 ± 0.63 mm across five expert-selected anatomic landmarks (in-plane misalignment of two to three pixels at 0.67- to 1.00-mm spatial resolution). Moreover, the T2-weighted intensity distributions were distinctly different when comparing fibrotic tissue to perirectal fat (as expected), but showed a marked overlap when comparing fibrotic tissue and residual rectal cancer. Conclusions: Our fusion methodology enabled successful and accurate localization of post-treatment effects on in vivo MRI.},\n author = {J.T. Antunes and S.E. Viswanath and J.T. Brady and B.P. Crawshaw and P.R. Ros and S.R. Steele and C.P. Delaney and R.M. Paspulati and J.E. Willis and A. Madabhushi},\n doi = {10.1016/j.acra.2017.12.006},\n issn = {18784046},\n issue = {7},\n journal = {Academic Radiology},\n keywords = {Radiology,coregistration,pathology,rectal cancer,treatment response},\n title = {Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings},\n volume = {25},\n year = {2018},\n}\n\n\n Rationale and Objectives: The objective of this study was to develop and quantitatively evaluate a radiology-pathology fusion method for spatially mapping tissue regions corresponding to different chemoradiation therapy-related effects from surgically excised whole-mount rectal cancer histopathology onto preoperative magnetic resonance imaging (MRI). Materials and Methods: This study included six subjects with rectal cancer treated with chemoradiation therapy who were then imaged with a 3-T T2-weighted MRI sequence, before undergoing mesorectal excision surgery. Excised rectal specimens were sectioned, stained, and digitized as two-dimensional (2D) whole-mount slides. Annotations of residual disease, ulceration, fibrosis, muscularis propria, mucosa, fat, inflammation, and pools of mucin were made by an expert pathologist on digitized slide images. An expert radiologist and pathologist jointly established corresponding 2D sections between MRI and pathology images, as well as identified a total of 10 corresponding landmarks per case (based on visually similar structures) on both modalities (five for driving registration and five for evaluating alignment). We spatially fused the in vivo MRI and ex vivo pathology images using landmark-based registration. This allowed us to spatially map detailed annotations from 2D pathology slides onto corresponding 2D MRI sections. Results: Quantitative assessment of coregistered pathology and MRI sections revealed excellent structural alignment, with an overall deviation of 1.50 ± 0.63 mm across five expert-selected anatomic landmarks (in-plane misalignment of two to three pixels at 0.67- to 1.00-mm spatial resolution). Moreover, the T2-weighted intensity distributions were distinctly different when comparing fibrotic tissue to perirectal fat (as expected), but showed a marked overlap when comparing fibrotic tissue and residual rectal cancer. Conclusions: Our fusion methodology enabled successful and accurate localization of post-treatment effects on in vivo MRI.\n
\n\n\n\n \n\n \n \n \n \n \n Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings.\n \n \n \n\n\n \n Penzias, G.; Singanamalli, A.; Elliott, R.; Gollamudi, J.; Shih, N.; Feldman, M.; Stricker, P.; Delprado, W.; Tiwari, S.; Böhm, M.; Haynes, A.; Ponsky, L.; Fu, P.; Tiwari, P.; Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n PLoS ONE, 13. 2018.\n \n\n\n\n
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\n\n\n@article{Penzias2018,\n abstract = {Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level.},\n author = {G. Penzias and A. Singanamalli and R.M. Elliott and J. Gollamudi and N.N. Shih and M.D. Feldman and P.D. Stricker and W.J. Delprado and S. Tiwari and M. Böhm and A.M. Haynes and L.E. Ponsky and P. Fu and P. Tiwari and S.E. Viswanath and A. Madabhushi},\n doi = {10.1371/journal.pone.0200730},\n issn = {19326203},\n issue = {8},\n journal = {PLoS ONE},\n title = {Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings},\n volume = {13},\n year = {2018},\n}\n\n\n Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level.\n
\n\n\n\n \n\n \n \n \n \n \n Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.\n \n \n \n\n\n \n Algohary, A.; Viswanath, S.; Shiradkar, R.; Ghose, S.; Pahwa, S.; Moses, D.; Jambor, I.; Shnier, R.; Böhm, M.; Haynes, A.; Brenner, P.; Delprado, W.; Thompson, J.; Pulbrock, M.; Purysko, A.; Verma, S.; Ponsky, L.; Stricker, P.; and Madabhushi, A.\n\n\n \n\n\n\n Journal of Magnetic Resonance Imaging, 48. 2018.\n \n\n\n\n
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\n\n\n@article{Algohary2018,\n abstract = {Background: Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS). Purpose: To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients. Study Type: Retrospective. Subjects Model: MRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy. Field Strength/Sequence: 3T, T<inf>2</inf>-weighted (T<inf>2</inf>w) and diffusion-weighted (DW) MRI. Assessment: A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T<inf>2</inf>w-MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI-negative–biopsy-negative (Group 1, N = 15), MRI-positive–biopsy-positive (Group 2, N = 16), MRI-negative–biopsy-positive (Group 3, N = 10), and MRI-positive–biopsy-negative (Group 4, N = 15). In all, 308 radiomic features (First-order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T<inf>2</inf>w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum-redundancy–maximum-relevance and used to construct three machine-learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease. Statistical Tests: Wilcoxon rank-sum tests with P < 0.05 considered statistically significant. Results: Seven T<inf>2</inf>w-based (First-order Statistics, Haralick, Laws, and Gabor) and three ADC-based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences (P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone. Data Conclusion: Radiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI-TRUS prostate biopsies. Level of Evidence: 3. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;48:818–828.},\n author = {A.O. Algohary and S.E. Viswanath and R. Shiradkar and S. Ghose and S. Pahwa and D.A. Moses and I. Jambor and R.C. Shnier and M. Böhm and A.M. Haynes and P.C. Brenner and W.J. Delprado and J.E. Thompson and M. Pulbrock and A.S. Purysko and S.M. Verma and L.E. Ponsky and P.D. Stricker and A. Madabhushi},\n doi = {10.1002/jmri.25983},\n issn = {15222586},\n issue = {3},\n journal = {Journal of Magnetic Resonance Imaging},\n keywords = {MRI,active surveillance,prostate cancer,radiomic features,radiomics,texture features},\n title = {Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings},\n volume = {48},\n year = {2018},\n}\n\n\n Background: Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS). Purpose: To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients. Study Type: Retrospective. Subjects Model: MRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy. Field Strength/Sequence: 3T, T2 -weighted (T2 w) and diffusion-weighted (DW) MRI. Assessment: A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T2 w-MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI-negative–biopsy-negative (Group 1, N = 15), MRI-positive–biopsy-positive (Group 2, N = 16), MRI-negative–biopsy-positive (Group 3, N = 10), and MRI-positive–biopsy-negative (Group 4, N = 15). In all, 308 radiomic features (First-order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T2 w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum-redundancy–maximum-relevance and used to construct three machine-learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease. Statistical Tests: Wilcoxon rank-sum tests with P < 0.05 considered statistically significant. Results: Seven T2 w-based (First-order Statistics, Haralick, Laws, and Gabor) and three ADC-based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences (P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone. Data Conclusion: Radiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI-TRUS prostate biopsies. Level of Evidence: 3. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;48:818–828.\n
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\n \n \n\n \n \n \n \n \n RADIomic spatial textural descriptor (RADISTAT): Characterizing intra-tumoral heterogeneity for response and outcome prediction.\n \n \n \n\n\n \n Antunes, J.; Prasanna, P.; Madabhushi, A.; Tiwari, P.; and Viswanath, S.\n\n\n \n\n\n\n 2017.\n \n\n\n\n
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\n\n\n@misc{Antunes2017,\n abstract = {Radiomic analysis in cancer applications enables capturing of disease-specific heterogeneity, through quantification of localized texture feature responses within and around a tumor region. Statistical descriptors of the resulting feature distribution (e.g. skewness, kurtosis) are then input to a predictive model. However, a single statistic may not fully capture the rich spatial diversity of pixel-wise radiomic expression maps. In this work, we present a new RADIomic Spatial TexturAl descripTor (RADISTAT) which attempts to (a) more completely characterize the spatial heterogeneity of a radiomic feature, and (b) capture the overall distribution heterogeneity of a radiomic feature by combining the proportion and arrangement of regions of high and low feature expression. We demonstrate the utility of RADISTAT in the context of (a) discriminating favorable from unfavorable treatment response in a cohort of N = 44 rectal cancer (RCa) patients, and (b) distinguishing short-term from long-term survivors in a cohort of N = 55 glioblastoma multiforme (GBM) patients. For both datasets, RADISTAT resulted in a significantly improved classification performance (AUC = 0.79 in the RCa cohort, AUC = 0.71 in the GBM cohort, based on randomized cross-validation) as compared to using simple statistics (mean, variance, skewness, or kurtosis) to describe radiomic co-occurrence features.},\n author = {J.T. Antunes and P. Prasanna and A. Madabhushi and P. Tiwari and S.E. Viswanath},\n doi = {10.1007/978-3-319-66185-8_53},\n isbn = {9783319661841},\n issn = {16113349},\n journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n title = {RADIomic spatial textural descriptor (RADISTAT): Characterizing intra-tumoral heterogeneity for response and outcome prediction},\n volume = {10434 LNCS},\n year = {2017},\n}\n\n\n Radiomic analysis in cancer applications enables capturing of disease-specific heterogeneity, through quantification of localized texture feature responses within and around a tumor region. Statistical descriptors of the resulting feature distribution (e.g. skewness, kurtosis) are then input to a predictive model. However, a single statistic may not fully capture the rich spatial diversity of pixel-wise radiomic expression maps. In this work, we present a new RADIomic Spatial TexturAl descripTor (RADISTAT) which attempts to (a) more completely characterize the spatial heterogeneity of a radiomic feature, and (b) capture the overall distribution heterogeneity of a radiomic feature by combining the proportion and arrangement of regions of high and low feature expression. We demonstrate the utility of RADISTAT in the context of (a) discriminating favorable from unfavorable treatment response in a cohort of N = 44 rectal cancer (RCa) patients, and (b) distinguishing short-term from long-term survivors in a cohort of N = 55 glioblastoma multiforme (GBM) patients. For both datasets, RADISTAT resulted in a significantly improved classification performance (AUC = 0.79 in the RCa cohort, AUC = 0.71 in the GBM cohort, based on randomized cross-validation) as compared to using simple statistics (mean, variance, skewness, or kurtosis) to describe radiomic co-occurrence features.\n
\n\n\n\n \n\n \n \n \n \n \n Optical high content nanoscopy of epigenetic marks decodes phenotypic divergence in stem cells.\n \n \n \n\n\n \n Kim, J.; Bennett, N.; Devita, M.; Chahar, S.; Viswanath, S.; Lee, E.; Jung, G.; Shao, P.; Childers, E.; Liu, S.; Kulesa, A.; Garcia, B.; Becker, M.; Hwang, N.; Madabhushi, A.; Verzi, M.; and Moghe, P.\n\n\n \n\n\n\n Scientific Reports, 7. 2017.\n \n\n\n\n
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\n\n\n@article{Kim2017,\n abstract = {While distinct stem cell phenotypes follow global changes in chromatin marks, single-cell chromatin technologies are unable to resolve or predict stem cell fates. We propose the first such use of optical high content nanoscopy of histone epigenetic marks (epi-marks) in stem cells to classify emergent cell states. By combining nanoscopy with epi-mark textural image informatics, we developed a novel approach, termed EDICTS (Epi-mark Descriptor Imaging of Cell Transitional States), to discern chromatin organizational changes, demarcate lineage gradations across a range of stem cell types and robustly track lineage restriction kinetics. We demonstrate the utility of EDICTS by predicting the lineage progression of stem cells cultured on biomaterial substrates with graded nanotopographies and mechanical stiffness, thus parsing the role of specific biophysical cues as sensitive epigenetic drivers. We also demonstrate the unique power of EDICTS to resolve cellular states based on epi-marks that cannot be detected via mass spectrometry based methods for quantifying the abundance of histone posttranslational modifications. Overall, EDICTS represents a powerful new methodology to predict single cell lineage decisions by integrating high content super-resolution nanoscopy and imaging informatics of the nuclear organization of epi-marks.},\n author = {J.J. Kim and N.K. Bennett and M.S. Devita and S. Chahar and S.E. Viswanath and E.A. Lee and G. Jung and P.P. Shao and E.P. Childers and S. Liu and A.B. Kulesa and B.A. Garcia and M.L. Becker and N.S.Y. Hwang and A. Madabhushi and M.P. Verzi and P.V. Moghe},\n doi = {10.1038/srep39406},\n issn = {20452322},\n journal = {Scientific Reports},\n title = {Optical high content nanoscopy of epigenetic marks decodes phenotypic divergence in stem cells},\n volume = {7},\n year = {2017},\n}\n\n\n While distinct stem cell phenotypes follow global changes in chromatin marks, single-cell chromatin technologies are unable to resolve or predict stem cell fates. We propose the first such use of optical high content nanoscopy of histone epigenetic marks (epi-marks) in stem cells to classify emergent cell states. By combining nanoscopy with epi-mark textural image informatics, we developed a novel approach, termed EDICTS (Epi-mark Descriptor Imaging of Cell Transitional States), to discern chromatin organizational changes, demarcate lineage gradations across a range of stem cell types and robustly track lineage restriction kinetics. We demonstrate the utility of EDICTS by predicting the lineage progression of stem cells cultured on biomaterial substrates with graded nanotopographies and mechanical stiffness, thus parsing the role of specific biophysical cues as sensitive epigenetic drivers. We also demonstrate the unique power of EDICTS to resolve cellular states based on epi-marks that cannot be detected via mass spectrometry based methods for quantifying the abundance of histone posttranslational modifications. Overall, EDICTS represents a powerful new methodology to predict single cell lineage decisions by integrating high content super-resolution nanoscopy and imaging informatics of the nuclear organization of epi-marks.\n
\n\n\n\n \n\n \n \n \n \n \n Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: Concepts, workflow, and use-cases.\n \n \n \n\n\n \n Viswanath, S.; Tiwari, P.; Lee, G.; and Madabhushi, A.\n\n\n \n\n\n\n BMC Medical Imaging, 17. 2017.\n \n\n\n\n
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\n\n\n@article{Viswanath2017,\n abstract = {Background: With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data being routinely acquired for disease characterization, there is a pressing need for quantitative tools to combine these varied channels of information. The goal of these integrated predictors is to combine these varied sources of information, while improving on the predictive ability of any individual modality. A number of application-specific data fusion methods have been previously proposed in the literature which have attempted to reconcile the differences in dimensionalities and length scales across different modalities. Our objective in this paper was to help identify metholodological choices that need to be made in order to build a data fusion technique, as it is not always clear which strategy is optimal for a particular problem. As a comprehensive review of all possible data fusion methods was outside the scope of this paper, we have focused on fusion approaches that employ dimensionality reduction (DR). Methods: In this work, we quantitatively evaluate 4 non-overlapping existing instantiations of DR-based data fusion, within 3 different biomedical applications comprising over 100 studies. These instantiations utilized different knowledge representation and knowledge fusion methods, allowing us to examine the interplay of these modules in the context of data fusion. The use cases considered in this work involve the integration of (a) radiomics features from T2w MRI with peak area features from MR spectroscopy for identification of prostate cancer in vivo, (b) histomorphometric features (quantitative features extracted from histopathology) with protein mass spectrometry features for predicting 5 year biochemical recurrence in prostate cancer patients, and (c) volumetric measurements on T1w MRI with protein expression features to discriminate between patients with and without Alzheimers' Disease. Results and conclusions: Our preliminary results in these specific use cases indicated that the use of kernel representations in conjunction with DR-based fusion may be most effective, as a weighted multi-kernel-based DR approach resulted in the highest area under the ROC curve of over 0.8. By contrast non-optimized DR-based representation and fusion methods yielded the worst predictive performance across all 3 applications. Our results suggest that when the individual modalities demonstrate relatively poor discriminability, many of the data fusion methods may not yield accurate, discriminatory representations either. In summary, to outperform the predictive ability of individual modalities, methodological choices for data fusion must explicitly account for the sparsity of and noise in the feature space.},\n author = {S.E. Viswanath and P. Tiwari and G. Lee and A. Madabhushi},\n doi = {10.1186/s12880-016-0172-6},\n issn = {14712342},\n issue = {1},\n journal = {BMC Medical Imaging},\n keywords = {Data fusion,Dimensionality reduction,Imaging,Kernels,Non-imaging},\n title = {Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: Concepts, workflow, and use-cases},\n volume = {17},\n year = {2017},\n}\n\n\n Background: With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data being routinely acquired for disease characterization, there is a pressing need for quantitative tools to combine these varied channels of information. The goal of these integrated predictors is to combine these varied sources of information, while improving on the predictive ability of any individual modality. A number of application-specific data fusion methods have been previously proposed in the literature which have attempted to reconcile the differences in dimensionalities and length scales across different modalities. Our objective in this paper was to help identify metholodological choices that need to be made in order to build a data fusion technique, as it is not always clear which strategy is optimal for a particular problem. As a comprehensive review of all possible data fusion methods was outside the scope of this paper, we have focused on fusion approaches that employ dimensionality reduction (DR). Methods: In this work, we quantitatively evaluate 4 non-overlapping existing instantiations of DR-based data fusion, within 3 different biomedical applications comprising over 100 studies. These instantiations utilized different knowledge representation and knowledge fusion methods, allowing us to examine the interplay of these modules in the context of data fusion. The use cases considered in this work involve the integration of (a) radiomics features from T2w MRI with peak area features from MR spectroscopy for identification of prostate cancer in vivo, (b) histomorphometric features (quantitative features extracted from histopathology) with protein mass spectrometry features for predicting 5 year biochemical recurrence in prostate cancer patients, and (c) volumetric measurements on T1w MRI with protein expression features to discriminate between patients with and without Alzheimers' Disease. Results and conclusions: Our preliminary results in these specific use cases indicated that the use of kernel representations in conjunction with DR-based fusion may be most effective, as a weighted multi-kernel-based DR approach resulted in the highest area under the ROC curve of over 0.8. By contrast non-optimized DR-based representation and fusion methods yielded the worst predictive performance across all 3 applications. Our results suggest that when the individual modalities demonstrate relatively poor discriminability, many of the data fusion methods may not yield accurate, discriminatory representations either. In summary, to outperform the predictive ability of individual modalities, methodological choices for data fusion must explicitly account for the sparsity of and noise in the feature space.\n
\n\n\n\n \n\n \n \n \n \n \n Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation.\n \n \n \n\n\n \n Li, L.; Pahwa, S.; Penzias, G.; Rusu, M.; Gollamudi, J.; Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n Scientific Reports, 7. 2017.\n \n\n\n\n
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\n\n\n@article{Li2017,\n abstract = {Multi-modal image co-registration via optimizing mutual information (MI) is based on the assumption that intensity distributions of multi-modal images follow a consistent relationship. However, images with a substantial difference in appearance violate this assumption, thus MI directly based on image intensity alone may be inadequate to drive similarity based co-registration. To address this issue, we introduce a novel approach for multi-modal co-registration called Multi-scale Spectral Embedding Registration (MSERg). MSERg involves the construction of multi-scale spectral embedding (SE) representations from multimodal images via texture feature extraction, scale selection, independent component analysis (ICA) and SE to create orthogonal representations that decrease the dissimilarity between the fixed and moving images to facilitate better co-registration. To validate the MSERg method, we aligned 45 pairs of in vivo prostate MRI and corresponding ex vivo histopathology images. The dataset was split into a learning set and a testing set. In the learning set, length scales of 5 × 5, 7 × 7 and 17 × 17 were selected. In the independent testing set, we compared MSERg with intensity-based registration, multi-attribute combined mutual information (MACMI) registration and scale-invariant feature transform (SIFT) flow registration. Our results suggest that multi-scale SE representations generated by MSERg are found to be more appropriate for radiology-pathology co-registration.},\n author = {L. Li and S. Pahwa and G. Penzias and M. Rusu and J. Gollamudi and S.E. Viswanath and A. Madabhushi},\n doi = {10.1038/s41598-017-08969-w},\n issn = {20452322},\n issue = {1},\n journal = {Scientific Reports},\n title = {Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation},\n volume = {7},\n year = {2017},\n}\n\n\n Multi-modal image co-registration via optimizing mutual information (MI) is based on the assumption that intensity distributions of multi-modal images follow a consistent relationship. However, images with a substantial difference in appearance violate this assumption, thus MI directly based on image intensity alone may be inadequate to drive similarity based co-registration. To address this issue, we introduce a novel approach for multi-modal co-registration called Multi-scale Spectral Embedding Registration (MSERg). MSERg involves the construction of multi-scale spectral embedding (SE) representations from multimodal images via texture feature extraction, scale selection, independent component analysis (ICA) and SE to create orthogonal representations that decrease the dissimilarity between the fixed and moving images to facilitate better co-registration. To validate the MSERg method, we aligned 45 pairs of in vivo prostate MRI and corresponding ex vivo histopathology images. The dataset was split into a learning set and a testing set. In the learning set, length scales of 5 × 5, 7 × 7 and 17 × 17 were selected. In the independent testing set, we compared MSERg with intensity-based registration, multi-attribute combined mutual information (MACMI) registration and scale-invariant feature transform (SIFT) flow registration. Our results suggest that multi-scale SE representations generated by MSERg are found to be more appropriate for radiology-pathology co-registration.\n
\n\n\n\n \n\n \n \n \n \n \n Discriminative scale learning (DiScrn): Applications to prostate cancer detection from MRI and needle biopsies.\n \n \n \n\n\n \n Wang, H.; Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n Scientific Reports, 7. 2017.\n \n\n\n\n
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\n\n\n@article{Wang2017,\n abstract = {There has been recent substantial interest in extracting sub-visual features from medical images for improved disease characterization compared to what might be achievable via visual inspection alone. Features such as Haralick and Gabor can provide a multi-scale representation of the original image by extracting measurements across differently sized neighborhoods. While these multi-scale features are effective, on large-scale digital pathological images, the process of extracting these features is computationally expensive. Moreover for different problems, different scales and neighborhood sizes may be more or less important and thus a large number of features extracted might end up being redundant. In this paper, we present a Discriminative Scale learning (DiScrn) approach that attempts to automatically identify the distinctive scales at which features are able to best separate cancerous from non-cancerous regions on both radiologic and digital pathology tissue images. To evaluate the efficacy of our approach, our approach was employed to detect presence and extent of prostate cancer on a total of 60 MRI and digitized histopathology images. Compared to a multi-scale feature analysis approach invoking features across all scales, DiScrn achieved 66% computational efficiency while also achieving comparable or even better classifier performance.},\n author = {H. Wang and S.E. Viswanath and A. Madabhushi},\n doi = {10.1038/s41598-017-12569-z},\n issn = {20452322},\n issue = {1},\n journal = {Scientific Reports},\n title = {Discriminative scale learning (DiScrn): Applications to prostate cancer detection from MRI and needle biopsies},\n volume = {7},\n year = {2017},\n}\n\n\n There has been recent substantial interest in extracting sub-visual features from medical images for improved disease characterization compared to what might be achievable via visual inspection alone. Features such as Haralick and Gabor can provide a multi-scale representation of the original image by extracting measurements across differently sized neighborhoods. While these multi-scale features are effective, on large-scale digital pathological images, the process of extracting these features is computationally expensive. Moreover for different problems, different scales and neighborhood sizes may be more or less important and thus a large number of features extracted might end up being redundant. In this paper, we present a Discriminative Scale learning (DiScrn) approach that attempts to automatically identify the distinctive scales at which features are able to best separate cancerous from non-cancerous regions on both radiologic and digital pathology tissue images. To evaluate the efficacy of our approach, our approach was employed to detect presence and extent of prostate cancer on a total of 60 MRI and digitized histopathology images. Compared to a multi-scale feature analysis approach invoking features across all scales, DiScrn achieved 66% computational efficiency while also achieving comparable or even better classifier performance.\n
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\n \n \n\n \n \n \n \n \n Multi-modality registration via multi-scale textural and spectral embedding representations.\n \n \n \n\n\n \n Li, L.; Rusu, M.; Viswanath, S.; Penzias, G.; Pahwa, S.; Gollamudi, J.; and Madabhushi, A.\n\n\n \n\n\n\n 2016.\n \n\n\n\n
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\n\n\n@misc{Li2016,\n abstract = {Intensity-based similarity measures assume that the original signal intensity of different modality images can provide statistically consistent information regarding the two modalities to be co-registered. In multi-modal registration problems, however, intensity-based similarity measures are often inadequate to identify an optimal transformation. Texture features can improve the performance of the multi-modal co-registration by providing more similar appearance representations of the two images to be co-registered, compared to the signal intensity representations. Furthermore, texture features extracted at different length scales (neighborhood sizes) can reveal similar underlying structural attributes between the images to be co-registered similarities that may not be discernible on the signal intensity representation alone. However one limitation of using texture features is that a number of them may be redundant and dependent and hence there is a need to identify non-redundant representations. Additionally it is not clear which features at which specific scales reveal similar attributes across the images to be co-registered. To address this problem, we introduced a novel approach for multimodal co-registration that employs new multi-scale image representations. Our approach comprises 4 distinct steps: (1) texure feature extraction at each length scale within both the target and template images, (2) independent component analysis (ICA) at each texture feature length scale, and (3) spectrally embedding (SE) the ICA components (ICs) obtained for the texture features at each length scale, and finally (4) identifying and combining the optimal length scales at which to perform the co-registration. To combine and co-register across different length scales, -mutual information (-MI) was applied in the high dimensional space of spectral embedding vectors to facilitate co-registration. To validate our multi-scale co-registration approach, we aligned 45 pairs of prostate MRI and histology images corresponding to the prostate with the objective of mapping extent of prostate cancer annotated by a pathologist on the pathology onto the pre-operative MRI. The registration results showed higher correlation between template and target images with average correlation ratio of 0.927 compared to 0.914 for intensity-based registration. Additionally an improvement in the dice similarity coefficient (DSC) of 13.6% was observed for the multi-scale registration compared to intensity-based registration and an 1.26% DSC improvement compared to registration involving the best individual scale.},\n author = {L. Li and M. Rusu and S.E. Viswanath and G. Penzias and S. Pahwa and J. Gollamudi and A. Madabhushi},\n doi = {10.1117/12.2217639},\n isbn = {9781510600195},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Co-registration,ICA,Spectral embedding,α MI},\n title = {Multi-modality registration via multi-scale textural and spectral embedding representations},\n volume = {9784},\n year = {2016},\n}\n\n\n Intensity-based similarity measures assume that the original signal intensity of different modality images can provide statistically consistent information regarding the two modalities to be co-registered. In multi-modal registration problems, however, intensity-based similarity measures are often inadequate to identify an optimal transformation. Texture features can improve the performance of the multi-modal co-registration by providing more similar appearance representations of the two images to be co-registered, compared to the signal intensity representations. Furthermore, texture features extracted at different length scales (neighborhood sizes) can reveal similar underlying structural attributes between the images to be co-registered similarities that may not be discernible on the signal intensity representation alone. However one limitation of using texture features is that a number of them may be redundant and dependent and hence there is a need to identify non-redundant representations. Additionally it is not clear which features at which specific scales reveal similar attributes across the images to be co-registered. To address this problem, we introduced a novel approach for multimodal co-registration that employs new multi-scale image representations. Our approach comprises 4 distinct steps: (1) texure feature extraction at each length scale within both the target and template images, (2) independent component analysis (ICA) at each texture feature length scale, and (3) spectrally embedding (SE) the ICA components (ICs) obtained for the texture features at each length scale, and finally (4) identifying and combining the optimal length scales at which to perform the co-registration. To combine and co-register across different length scales, -mutual information (-MI) was applied in the high dimensional space of spectral embedding vectors to facilitate co-registration. To validate our multi-scale co-registration approach, we aligned 45 pairs of prostate MRI and histology images corresponding to the prostate with the objective of mapping extent of prostate cancer annotated by a pathologist on the pathology onto the pre-operative MRI. The registration results showed higher correlation between template and target images with average correlation ratio of 0.927 compared to 0.914 for intensity-based registration. Additionally an improvement in the dice similarity coefficient (DSC) of 13.6% was observed for the multi-scale registration compared to intensity-based registration and an 1.26% DSC improvement compared to registration involving the best individual scale.\n
\n\n\n\n \n\n \n \n \n \n \n Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: A proof-of-concept study.\n \n \n \n\n\n \n Antunes, J.; Viswanath, S.; Rusu, M.; Valls, L.; Hoimes, C.; Avril, N.; and Madabhushi, A.\n\n\n \n\n\n\n Translational Oncology, 9. 2016.\n \n\n\n\n
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\n\n\n@article{Antunes2016,\n abstract = {Studying early response to cancer treatment is significant for patient treatment stratification and follow-up. Although recent advances in positron emission tomography (PET) and magnetic resonance imaging (MRI) allow for evaluation of tumor response, a quantitative objective assessment of treatment-related effects offers localization and quantification of structural and functional changes in the tumor region. Radiomics, the process of computerized extraction of features from radiographic images, is a new strategy for capturing subtle changes in the tumor region that works by quantifying subvisual patterns which might escape human identification. The goal of this study was to demonstrate feasibility for performing radiomics analysis on integrated PET/MRI to characterize early treatment response in metastatic renal cell carcinoma (RCC) undergoing sunitinib therapy. Two patients with advanced RCC were imaged using an integrated PET/MRI scanner. [18 F] fluorothymidine (FLT) was used as the PET radiotracer, which can measure the degree of cell proliferation. Image acquisitions included test/ retest scans before sunitinib treatment and one scan 3 weeks into treatment using [18 F] FLT-PET, T2-weighted (T2w), and diffusion-weighted imaging (DWI) protocols, where DWI yielded an apparent diffusion coefficient (ADC) map. Our framework to quantitatively characterize treatment-related changes involved the following analytic steps: 1) intraacquisition and interacquisition registration of protocols to allow voxel-wise comparison of changes in radiomic features, 2) correction and pseudoquantification of T2w images to remove acquisition artifacts and examine tissue-specific response, 3) characterization of information captured by T2w MRI, FLT-PET, and ADC via radiomics, and 4) combining multiparametric information to create a map of integrated changes from PET/MRI radiomic features. Standardized uptake value (from FLT-PET) and ADC textures ranked highest for reproducibility in a test/retest evaluation as well as for capturing treatment response, in comparison to high variability seen in T2w MRI. The highest-ranked radiomic feature yielded a normalized percentage change of 63% within the RCC region and 17% in a spatially distinct normal region relative to its pretreatment value. By comparison, both the original and postprocessed T2w signal intensity appeared to be markedly less sensitive and specific to changes within the tumor. Our preliminary results thus suggest that radiomics analysis could be a powerful tool for characterizing treatment response in integrated PET/MRI.},\n author = {J.T. Antunes and S.E. Viswanath and M. Rusu and L. Valls and C.J. Hoimes and N.E. Avril and A. Madabhushi},\n doi = {10.1016/j.tranon.2016.01.008},\n issn = {19365233},\n issue = {2},\n journal = {Translational Oncology},\n title = {Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: A proof-of-concept study},\n volume = {9},\n year = {2016},\n}\n\n\n Studying early response to cancer treatment is significant for patient treatment stratification and follow-up. Although recent advances in positron emission tomography (PET) and magnetic resonance imaging (MRI) allow for evaluation of tumor response, a quantitative objective assessment of treatment-related effects offers localization and quantification of structural and functional changes in the tumor region. Radiomics, the process of computerized extraction of features from radiographic images, is a new strategy for capturing subtle changes in the tumor region that works by quantifying subvisual patterns which might escape human identification. The goal of this study was to demonstrate feasibility for performing radiomics analysis on integrated PET/MRI to characterize early treatment response in metastatic renal cell carcinoma (RCC) undergoing sunitinib therapy. Two patients with advanced RCC were imaged using an integrated PET/MRI scanner. [18 F] fluorothymidine (FLT) was used as the PET radiotracer, which can measure the degree of cell proliferation. Image acquisitions included test/ retest scans before sunitinib treatment and one scan 3 weeks into treatment using [18 F] FLT-PET, T2-weighted (T2w), and diffusion-weighted imaging (DWI) protocols, where DWI yielded an apparent diffusion coefficient (ADC) map. Our framework to quantitatively characterize treatment-related changes involved the following analytic steps: 1) intraacquisition and interacquisition registration of protocols to allow voxel-wise comparison of changes in radiomic features, 2) correction and pseudoquantification of T2w images to remove acquisition artifacts and examine tissue-specific response, 3) characterization of information captured by T2w MRI, FLT-PET, and ADC via radiomics, and 4) combining multiparametric information to create a map of integrated changes from PET/MRI radiomic features. Standardized uptake value (from FLT-PET) and ADC textures ranked highest for reproducibility in a test/retest evaluation as well as for capturing treatment response, in comparison to high variability seen in T2w MRI. The highest-ranked radiomic feature yielded a normalized percentage change of 63% within the RCC region and 17% in a spatially distinct normal region relative to its pretreatment value. By comparison, both the original and postprocessed T2w signal intensity appeared to be markedly less sensitive and specific to changes within the tumor. Our preliminary results thus suggest that radiomics analysis could be a powerful tool for characterizing treatment response in integrated PET/MRI.\n
\n\n\n\n \n\n \n \n \n \n \n AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments.\n \n \n \n\n\n \n Penzias, G.; Janowczyk, A.; Singanamalli, A.; Rusu, M.; Shih, N.; Feldman, M.; Stricker, P.; Delprado, W.; Tiwari, S.; Böhm, M.; Haynes, A.; Ponsky, L.; Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n Scientific Reports, 6. 2016.\n \n\n\n\n
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\n\n\n@article{Penzias2016,\n abstract = {In applications involving large tissue specimens that have been sectioned into smaller tissue fragments, manual reconstruction of a "pseudo whole-mount" histological section (PWMHS) can facilitate (a) pathological disease annotation, and (b) image registration and correlation with radiological images. We have previously presented a program called HistoStitcher, which allows for more efficient manual reconstruction than general purpose image editing tools (such as Photoshop). However HistoStitcher is still manual and hence can be laborious and subjective, especially when doing large cohort studies. In this work we present AutoStitcher, a novel automated algorithm for reconstructing PWMHSs from digitized tissue fragments. AutoStitcher reconstructs ("stitches") a PWMHS from a set of 4 fragments by optimizing a novel cost function that is domain-inspired to ensure (i) alignment of similar tissue regions, and (ii) contiguity of the prostate boundary. The algorithm achieves computational efficiency by performing reconstruction in a multi-resolution hierarchy. Automated PWMHS reconstruction results (via AutoStitcher) were quantitatively and qualitatively compared to manual reconstructions obtained via HistoStitcher for 113 prostate pathology sections. Distances between corresponding fiducials placed on each of the automated and manual reconstruction results were between 2.7%-3.2%, reflecting their excellent visual similarity.},\n author = {G. Penzias and A.R. Janowczyk and A. Singanamalli and M. Rusu and N.N. Shih and M.D. Feldman and P.D. Stricker and W.J. Delprado and S. Tiwari and M. Böhm and A.M. Haynes and L.E. Ponsky and S.E. Viswanath and A. Madabhushi},\n doi = {10.1038/srep29906},\n issn = {20452322},\n journal = {Scientific Reports},\n title = {AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments},\n volume = {6},\n year = {2016},\n}\n\n\n In applications involving large tissue specimens that have been sectioned into smaller tissue fragments, manual reconstruction of a \"pseudo whole-mount\" histological section (PWMHS) can facilitate (a) pathological disease annotation, and (b) image registration and correlation with radiological images. We have previously presented a program called HistoStitcher, which allows for more efficient manual reconstruction than general purpose image editing tools (such as Photoshop). However HistoStitcher is still manual and hence can be laborious and subjective, especially when doing large cohort studies. In this work we present AutoStitcher, a novel automated algorithm for reconstructing PWMHSs from digitized tissue fragments. AutoStitcher reconstructs (\"stitches\") a PWMHS from a set of 4 fragments by optimizing a novel cost function that is domain-inspired to ensure (i) alignment of similar tissue regions, and (ii) contiguity of the prostate boundary. The algorithm achieves computational efficiency by performing reconstruction in a multi-resolution hierarchy. Automated PWMHS reconstruction results (via AutoStitcher) were quantitatively and qualitatively compared to manual reconstructions obtained via HistoStitcher for 113 prostate pathology sections. Distances between corresponding fiducials placed on each of the automated and manual reconstruction results were between 2.7%-3.2%, reflecting their excellent visual similarity.\n
\n\n\n\n \n\n \n \n \n \n \n Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI.\n \n \n \n\n\n \n Shiradkar, R.; Podder, T.; Algohary, A.; Viswanath, S.; Ellis, R.; and Madabhushi, A.\n\n\n \n\n\n\n Radiation Oncology, 11. 2016.\n \n\n\n\n
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\n\n\n@article{Shiradkar2016,\n abstract = {Background: Radiomics or computer - extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. Methods: The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. Results: Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous (ℙ<sup>WH</sup>) which is the current clinical standard, radiomics based focal (ℙ<sup>RF</sup>), and whole gland with a radiomics based focal boost (ℙ<sup>WH</sup>). Comparison of ℙ<sup>RF</sup> against conventional ℙ<sup>WH</sup> revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. ℙ<sup>WH</sup> resulted in only a marginal increase in dosage to the OARs compared to ℙ<sup>WH</sup>. A similar trend was observed in case of EBRT with ℙ<sup>WH</sup> and ℙ<sup>WH</sup> compared to ℙ<sup>WH</sup>. Conclusions: A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.},\n author = {R. Shiradkar and T.K. Podder and A.O. Algohary and S.E. Viswanath and R.J. Ellis and A. Madabhushi},\n doi = {10.1186/s13014-016-0718-3},\n issn = {1748717X},\n issue = {1},\n journal = {Radiation Oncology},\n keywords = {Computer aided diagnosis (CAD),Prostate cancer,Radiomics,Treatment planning},\n title = {Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI},\n volume = {11},\n year = {2016},\n}\n\n\n Background: Radiomics or computer - extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. Methods: The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. Results: Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous (ℙWH) which is the current clinical standard, radiomics based focal (ℙRF), and whole gland with a radiomics based focal boost (ℙWH). Comparison of ℙRF against conventional ℙWH revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. ℙWH resulted in only a marginal increase in dosage to the OARs compared to ℙWH. A similar trend was observed in case of EBRT with ℙWH and ℙWH compared to ℙWH. Conclusions: A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.\n
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\n \n \n\n \n \n \n \n \n Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.\n \n \n \n\n\n \n Ginsburg, S.; Viswanath, S.; Bloch, B.; Rofsky, N.; Genega, E.; Lenkinski, R.; and Madabhushi, A.\n\n\n \n\n\n\n Journal of Magnetic Resonance Imaging, 41. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{Ginsburg2015,\n abstract = {Purpose To identify computer-extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI). Materials and Methods Preoperative T2-weighted (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI were acquired from 23 men with confirmed prostate cancer. Following radical prostatectomy, the cancer extent was delineated by a pathologist on ex vivo histology and mapped to MRI by nonlinear registration of histology and corresponding MRI slices. In all, 244 computer-extracted features were extracted from MRI, and principal component analysis (PCA) was employed to reduce the data dimensionality so that a generalizable classifier could be constructed. A novel variable importance on projection (VIP) measure for PCA (PCA-VIP) was leveraged to identify computer-extracted MRI features that discriminate between cancer and normal prostate, and these features were used to construct classifiers for cancer localization. Results Classifiers using features selected by PCA-VIP yielded an area under the curve (AUC) of 0.79 and 0.85 for peripheral zone and central gland tumors, respectively. For tumor localization in the central gland, T2w, DCE, and DWI MRI features contributed 71.6%, 18.1%, and 10.2%, respectively; for peripheral zone tumors T2w, DCE, and DWI MRI contributed 29.6%, 21.7%, and 48.7%, respectively. Conclusion PCA-VIP identified relatively stable subsets of MRI features that performed well in localizing prostate cancer on MRI. J. Magn. Reson. Imaging 2015;41:1383-1393.},\n author = {S.B. Ginsburg and S.E. Viswanath and B.N. Bloch and N.M. Rofsky and E.M. Genega and R.E. Lenkinski and A. Madabhushi},\n doi = {10.1002/jmri.24676},\n issn = {15222586},\n issue = {5},\n journal = {Journal of Magnetic Resonance Imaging},\n keywords = {computer-extracted features,feature selection,model interpretation,principal component analysis,prostate cancer},\n title = {Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors},\n volume = {41},\n year = {2015},\n}\n\n\n Purpose To identify computer-extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI). Materials and Methods Preoperative T2-weighted (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI were acquired from 23 men with confirmed prostate cancer. Following radical prostatectomy, the cancer extent was delineated by a pathologist on ex vivo histology and mapped to MRI by nonlinear registration of histology and corresponding MRI slices. In all, 244 computer-extracted features were extracted from MRI, and principal component analysis (PCA) was employed to reduce the data dimensionality so that a generalizable classifier could be constructed. A novel variable importance on projection (VIP) measure for PCA (PCA-VIP) was leveraged to identify computer-extracted MRI features that discriminate between cancer and normal prostate, and these features were used to construct classifiers for cancer localization. Results Classifiers using features selected by PCA-VIP yielded an area under the curve (AUC) of 0.79 and 0.85 for peripheral zone and central gland tumors, respectively. For tumor localization in the central gland, T2w, DCE, and DWI MRI features contributed 71.6%, 18.1%, and 10.2%, respectively; for peripheral zone tumors T2w, DCE, and DWI MRI contributed 29.6%, 21.7%, and 48.7%, respectively. Conclusion PCA-VIP identified relatively stable subsets of MRI features that performed well in localizing prostate cancer on MRI. J. Magn. Reson. Imaging 2015;41:1383-1393.\n
\n\n\n\n \n\n \n \n \n \n \n Predicting classifier performance with limited training data: Applications to computer-aided diagnosis in breast and prostate cancer.\n \n \n \n\n\n \n Basavanhally, A.; Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n PLoS ONE, 10. 2015.\n \n\n\n\n
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\n\n\n@article{Basavanhally2015,\n abstract = {Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets.},\n author = {A.N. Basavanhally and S.E. Viswanath and A. Madabhushi},\n doi = {10.1371/journal.pone.0117900},\n issn = {19326203},\n issue = {5},\n journal = {PLoS ONE},\n title = {Predicting classifier performance with limited training data: Applications to computer-aided diagnosis in breast and prostate cancer},\n volume = {10},\n year = {2015},\n}\n\n\n Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets.\n
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\n \n \n\n \n \n \n \n \n Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation.\n \n \n \n\n\n \n Litjens, G.; Huisman, H.; Elliott, R.; Shih, N.; Feldman, M.; Viswanath, S.; Fütterer, J.; Bomers, J.; and Madabhushi, A.\n\n\n \n\n\n\n 2014.\n \n\n\n\n
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\n\n\n@misc{Litjens2014,\n abstract = {Laser interstitial thermotherapy (LITT) is a relatively new focal therapy technique for the ablation of localized prostate cancer. However, very little is known about the specific effects of LITT within the ablation zone and the surrounding normal tissue regions. For instance, it is important to be able to assess the extent of residual cancer within the prostate following LITT, which may be masked by thermally induced benign necrotic changes. Fortunately LITT is MRI compatible and hence this allows for quantitatively assessing LITT induced changes via multi-parametric MRI. Of course definite validation of any LITT induced changes on MRI requires confirmation via histopathology. The aim of this study was to quantitatively assess and distinguish the imaging characteristics of prostate cancer and benign confounding treatment changes following LITTon 3 Tesla multi-parametric MRI by carefully mapping the treatment related changes from the ex vivo surgically resected histopathologic specimens onto the pre-operative in vivo imaging. A better understanding of the imaging characteristics of residual disease and successfully ablated tissue might lead to improved treatment monitoring and as such patient prognosis. A unique clinical trial at the Radboud University Medical Center, in which 3 patients underwent a prostatectomy after LITT treatment, yielded ex-vivo histopathologic specimens along with pre- and post-LITT MRI. Using this data we (1) identified the computer extracted MRI signatures associated with treatment effects including benign necrotic changes and residual disease and (2) subsequently evaluated the computer extracted MRI features previously identified in distinguishing LITT induced changes in the ablated area relative to the residual disease. Towards this end first a pathologist annotated the ablated area and the residual disease on the ex-vivo histology and then we transferred the annotations to the post-LITT MRI using semi-automatic elastic registration. The pre- and post-LITT MRI were subsequently registered and computer-derived multi-parametric MRI features extracted to determine differences in feature values between residual disease and successfully ablated tissue to assess treatment response. A scoring metric allowed us to identify those specific computer-extracted MRI features that maximally and differentially expressed between the ablated regions and the residual cancer, on a voxel-by-voxel basis. Finally, we used a Fuzzy C-Means algorithm to assess the discriminatory power of these selected features. Our results show that specific computer-extracted features from multi-parametric MRI differentially express within the ablated and residual cancer regions, as evidenced by our ability to, on a voxel-by-voxel basis, classify tissue as residual disease. Additionally, we show that change of feature values between pre- and post-LITT MRI may be useful as a quantitative marker for treatment response (T2-weighted texture and DCE MRI features showed largest differences between residual disease and successfully ablated tissue). Finally, a clustering approach to separate treatment effects and residual disease incorporating both (1) and (2) yielded a maximum area under the ROC curve of 0.97 on a voxel basis across 3 studies. © 2014 SPIE.},\n author = {G.J. Litjens and H.J.J. Huisman and R.M. Elliott and N.N. Shih and M.D. Feldman and S.E. Viswanath and J.J. Fütterer and J.G. Bomers and A. Madabhushi},\n doi = {10.1117/12.2043819},\n isbn = {9780819498298},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Laser ablation therapy,MRI,prostate cancer,treatment response},\n title = {Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation},\n volume = {9036},\n year = {2014},\n}\n\n\n Laser interstitial thermotherapy (LITT) is a relatively new focal therapy technique for the ablation of localized prostate cancer. However, very little is known about the specific effects of LITT within the ablation zone and the surrounding normal tissue regions. For instance, it is important to be able to assess the extent of residual cancer within the prostate following LITT, which may be masked by thermally induced benign necrotic changes. Fortunately LITT is MRI compatible and hence this allows for quantitatively assessing LITT induced changes via multi-parametric MRI. Of course definite validation of any LITT induced changes on MRI requires confirmation via histopathology. The aim of this study was to quantitatively assess and distinguish the imaging characteristics of prostate cancer and benign confounding treatment changes following LITTon 3 Tesla multi-parametric MRI by carefully mapping the treatment related changes from the ex vivo surgically resected histopathologic specimens onto the pre-operative in vivo imaging. A better understanding of the imaging characteristics of residual disease and successfully ablated tissue might lead to improved treatment monitoring and as such patient prognosis. A unique clinical trial at the Radboud University Medical Center, in which 3 patients underwent a prostatectomy after LITT treatment, yielded ex-vivo histopathologic specimens along with pre- and post-LITT MRI. Using this data we (1) identified the computer extracted MRI signatures associated with treatment effects including benign necrotic changes and residual disease and (2) subsequently evaluated the computer extracted MRI features previously identified in distinguishing LITT induced changes in the ablated area relative to the residual disease. Towards this end first a pathologist annotated the ablated area and the residual disease on the ex-vivo histology and then we transferred the annotations to the post-LITT MRI using semi-automatic elastic registration. The pre- and post-LITT MRI were subsequently registered and computer-derived multi-parametric MRI features extracted to determine differences in feature values between residual disease and successfully ablated tissue to assess treatment response. A scoring metric allowed us to identify those specific computer-extracted MRI features that maximally and differentially expressed between the ablated regions and the residual cancer, on a voxel-by-voxel basis. Finally, we used a Fuzzy C-Means algorithm to assess the discriminatory power of these selected features. Our results show that specific computer-extracted features from multi-parametric MRI differentially express within the ablated and residual cancer regions, as evidenced by our ability to, on a voxel-by-voxel basis, classify tissue as residual disease. Additionally, we show that change of feature values between pre- and post-LITT MRI may be useful as a quantitative marker for treatment response (T2-weighted texture and DCE MRI features showed largest differences between residual disease and successfully ablated tissue). Finally, a clustering approach to separate treatment effects and residual disease incorporating both (1) and (2) yielded a maximum area under the ROC curve of 0.97 on a voxel basis across 3 studies. © 2014 SPIE.\n
\n\n\n\n \n\n \n \n \n \n \n Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy.\n \n \n \n\n\n \n Litjens, G.; Huisman, H.; Elliott, R.; Shih, N.; Feldman, M.; Viswanath, S.; Fütterer, J.; Bomers, J.; and Madabhushi, A.\n\n\n \n\n\n\n Journal of Medical Imaging, 1. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{Litjens2014,\n abstract = {Laser interstitial thermotherapy (LITT) is a relatively new focal therapy technique for the ablation of localized prostate cancer. In this study, for the first time, we are integrating ex vivo pathology and magnetic resonance imaging (MRI) to assess the imaging characteristics of prostate cancer and treatment changes following LITT. Via a unique clinical trial, which gave us the availability of ex vivo histology and pre- and post-LITT MRIs, (1) we investigated the imaging characteristics of treatment effects and residual disease, and (2) evaluated treatment-induced feature changes in the ablated area relative to the residual disease. First, a pathologist annotated the ablated area and the residual disease on the ex vivo histology. Subsequently, we transferred the annotations to the post-LITT MRI using a semi-automatic elastic registration. The pre- and post-LITT MRIs were registered and features were extracted. A scoring metric based on the change in median pre- and post-LITT feature values was introduced, which allowed us to identify the most treatment responsive features. Our results show that (1) image characteristics for treatment effects and residual disease are different, and (2) the change of feature values between pre- and post-LITT MRIs can be a quantitative biomarker for treatment response. Finally, using feature change improved discrimination between the residual disease and treatment effects.},\n author = {G.J. Litjens and H.J.J. Huisman and R.M. Elliott and N.N. Shih and M.D. Feldman and S.E. Viswanath and J.J. Fütterer and J.G. Bomers and A. Madabhushi},\n doi = {10.1117/1.JMI.1.3.035001},\n issn = {23294310},\n issue = {3},\n journal = {Journal of Medical Imaging},\n keywords = {laser ablation therapy,magnetic resonance imaging,prostate cancer,treatment response},\n title = {Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy},\n volume = {1},\n year = {2014},\n}\n\n\n Laser interstitial thermotherapy (LITT) is a relatively new focal therapy technique for the ablation of localized prostate cancer. In this study, for the first time, we are integrating ex vivo pathology and magnetic resonance imaging (MRI) to assess the imaging characteristics of prostate cancer and treatment changes following LITT. Via a unique clinical trial, which gave us the availability of ex vivo histology and pre- and post-LITT MRIs, (1) we investigated the imaging characteristics of treatment effects and residual disease, and (2) evaluated treatment-induced feature changes in the ablated area relative to the residual disease. First, a pathologist annotated the ablated area and the residual disease on the ex vivo histology. Subsequently, we transferred the annotations to the post-LITT MRI using a semi-automatic elastic registration. The pre- and post-LITT MRIs were registered and features were extracted. A scoring metric based on the change in median pre- and post-LITT feature values was introduced, which allowed us to identify the most treatment responsive features. Our results show that (1) image characteristics for treatment effects and residual disease are different, and (2) the change of feature values between pre- and post-LITT MRIs can be a quantitative biomarker for treatment response. Finally, using feature change improved discrimination between the residual disease and treatment effects.\n
\n\n\n\n \n\n \n \n \n \n \n Identifying quantitative in vivo multi-parametric MRI features for treatment related changes after laser interstitial thermal therapy of prostate cancer.\n \n \n \n\n\n \n Viswanath, S.; Toth, R.; Rusu, M.; Sperling, D.; Lepor, H.; Fütterer, J.; and Madabhushi, A.\n\n\n \n\n\n\n Neurocomputing, 144. 2014.\n \n\n\n\n
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\n\n\n@article{Viswanath2014,\n abstract = {Laser interstitial thermal therapy (LITT) is a new therapeutic strategy being explored in prostate cancer (CaP), which involves focal ablation of organ-localized tumor via an interstitial laser fiber. While little is known about treatment-related changes following LITT, studying post-LITT changes via imaging is extremely significant for enabling early image-guided intervention and follow-up. In this work, we present the first attempt at examining focal treatment-related changes on a per-voxel basis via quantitative comparison of MRI features pre- and post-LITT, and hence identifying computerized MRI features that are highly sensitive as well as specific to post-LITT changes within the ablation zone in the prostate. A retrospective cohort of 5 patient datasets comprising both pre- and post-LITT T2-weighted (T2w) and diffusion-weighted (DWI) acquisitions was considered, where DWI MRI yielded an Apparent Diffusion Co-efficient (ADC) map. Our scheme involved (1) inter-protocol registration of T2w and ADC MRI, as well as inter-acquisition registration of pre- and post-LITT MRI, (2) quantitation of MRI parameters by correcting for intensity drift in order to examine tissue-specific response, and (3) quantification of the information captured by T2w MRI and ADC maps via texture and intensity features. Correction of parameter drift resulted in visually discernible improvements in highlighting tissue-specific response in different MRI features. Quantitative, voxel-wise comparison of the changes in different MRI features indicated that steerable and non-steerable gradient texture features, rather than the original T2w intensity and ADC values, were highly sensitive as well as specific in identifying changes within the ablation zone pre- and post-LITT. The highest ranked texture feature yielded a normalized percentage change of 186% within the ablation zone and 43% in a spatially distinct normal region, relative to its pre-LITT value. By comparison, both the original T2w intensity and the ADC value demonstrated a markedly less sensitive and specific response to changes within the ablation zone. Qualitative as well as quantitative evaluation of co-occurrence texture features indicated the presence of LITT-related effects such as edema adjacent to the ablation zone, which were indiscernible on the original T2w and ADC images. Our preliminary results thus indicate great potential for non-invasive computerized MRI imaging features for determining focal treatment related changes, informing image-guided interventions, as well as predicting long- and short-term patient outcome. © 2014 Elsevier B.V.},\n author = {S.E. Viswanath and R. Toth and M. Rusu and D.S. Sperling and H.A. Lepor and J.J. Fütterer and A. Madabhushi},\n doi = {10.1016/j.neucom.2014.03.065},\n issn = {18728286},\n journal = {Neurocomputing},\n keywords = {Focal treatment,Image registration,Laser interstitial thermal therapy,Multi-parametric MRI,Prostate cancer,Treatment evaluation},\n title = {Identifying quantitative in vivo multi-parametric MRI features for treatment related changes after laser interstitial thermal therapy of prostate cancer},\n volume = {144},\n year = {2014},\n}\n\n\n Laser interstitial thermal therapy (LITT) is a new therapeutic strategy being explored in prostate cancer (CaP), which involves focal ablation of organ-localized tumor via an interstitial laser fiber. While little is known about treatment-related changes following LITT, studying post-LITT changes via imaging is extremely significant for enabling early image-guided intervention and follow-up. In this work, we present the first attempt at examining focal treatment-related changes on a per-voxel basis via quantitative comparison of MRI features pre- and post-LITT, and hence identifying computerized MRI features that are highly sensitive as well as specific to post-LITT changes within the ablation zone in the prostate. A retrospective cohort of 5 patient datasets comprising both pre- and post-LITT T2-weighted (T2w) and diffusion-weighted (DWI) acquisitions was considered, where DWI MRI yielded an Apparent Diffusion Co-efficient (ADC) map. Our scheme involved (1) inter-protocol registration of T2w and ADC MRI, as well as inter-acquisition registration of pre- and post-LITT MRI, (2) quantitation of MRI parameters by correcting for intensity drift in order to examine tissue-specific response, and (3) quantification of the information captured by T2w MRI and ADC maps via texture and intensity features. Correction of parameter drift resulted in visually discernible improvements in highlighting tissue-specific response in different MRI features. Quantitative, voxel-wise comparison of the changes in different MRI features indicated that steerable and non-steerable gradient texture features, rather than the original T2w intensity and ADC values, were highly sensitive as well as specific in identifying changes within the ablation zone pre- and post-LITT. The highest ranked texture feature yielded a normalized percentage change of 186% within the ablation zone and 43% in a spatially distinct normal region, relative to its pre-LITT value. By comparison, both the original T2w intensity and the ADC value demonstrated a markedly less sensitive and specific response to changes within the ablation zone. Qualitative as well as quantitative evaluation of co-occurrence texture features indicated the presence of LITT-related effects such as edema adjacent to the ablation zone, which were indiscernible on the original T2w and ADC images. Our preliminary results thus indicate great potential for non-invasive computerized MRI imaging features for determining focal treatment related changes, informing image-guided interventions, as well as predicting long- and short-term patient outcome. © 2014 Elsevier B.V.\n
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\n \n \n\n \n \n \n \n \n Quantitative evaluation of treatment related changes on multi-parametric MRI after laser interstitial thermal therapy of prostate cancer.\n \n \n \n\n\n \n Viswanath, S.; Toth, R.; Rusu, M.; Sperling, D.; Lepor, H.; Fütterer, J.; and Madabhushi, A.\n\n\n \n\n\n\n 2013.\n \n\n\n\n
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\n\n\n@misc{Viswanath2013,\n abstract = {Laser interstitial thermal therapy (LITT) has recently shown great promise as a treatment strategy for localized, focal, low-grade, organ-confined prostate cancer (CaP). Additionally, LITT is compatible with multi-parametric magnetic resonance imaging (MP-MRI) which in turn enables (1) high resolution, accurate localization of ablation zones on in vivo MP-MRI prior to LITT, and (2) real-time monitoring of temperature changes in vivo via MR thermometry during LITT. In spite of rapidly increasing interest in the use of LITT for treating low grade, focal CaP, very little is known about treatment-related changes following LITT. There is thus a clear need for studying post-LITT changes via MP-MRI and consequently to attempt to (1) quantitatively identify MP-MRI markers predictive of favorable treatment response and longer term patient outcome, and (2) identify which MP-MRI markers are most sensitive to post-LITT changes in the prostate. In this work, we present the first attempt at examining focal treatment-related changes on a per-voxel basis (high resolution) via quantitative evaluation of MR parameters pre- and post-LITT. A retrospective cohort of MP-MRI data comprising both pre- and post-LITT T2-weighted (T2w) and diffusion-weighted (DWI) acquisitions was considered, where DWI MRI yielded an Apparent Diffusion Co-efficient (ADC) map. A spatially constrained affine registration scheme was implemented to first bring T2w and ADC images into alignment within each of the pre- and post-LITT acquisitions, following which the pre- and post-LITT acquisitions were aligned. Pre- and post-LITT MR parameters (T2w intensity, ADC value) were then standardized to a uniform scale (to correct for intensity drift) and then quantified via the raw intensity values as well as via texture features derived from T2w MRI. In order to quantify imaging changes as a result of LITT, absolute differences were calculated between the normalized pre- and post-LITT MRI parameters. Quantitatively combining the ADC and T2w MRI parameters enabled construction of an integrated MP-MRI difference map that was highly indicative of changes specific to the LITT ablation zone. Preliminary quantitative comparison of the changes in different MR parameters indicated that T2w texture may be highly sensitive as well as specific in identifying changes within the ablation zone pre- and post-LITT. Visual evaluation of the differences in T2w texture features pre- and post-LITT also appeared to provide an indication of LITT-related effects such as edema. Our preliminary results thus indicate great potential for non-invasive MP-MRI imaging markers for determining focal treatment related changes, and hence long- and short-term patient outcome. © 2013 SPIE.},\n author = {S.E. Viswanath and R. Toth and M. Rusu and D.S. Sperling and H.A. Lepor and J.J. Fütterer and A. Madabhushi},\n doi = {10.1117/12.2008037},\n isbn = {9780819494450},\n issn = {0277786X},\n journal = {Proceedings of SPIE - The International Society for Optical Engineering},\n keywords = {Focal treatment,Laser interstitial thermal therapy,Multi-parametric MRI,Prostate cancer,Registration,Treatment change},\n title = {Quantitative evaluation of treatment related changes on multi-parametric MRI after laser interstitial thermal therapy of prostate cancer},\n volume = {8671},\n year = {2013},\n}\n\n\n Laser interstitial thermal therapy (LITT) has recently shown great promise as a treatment strategy for localized, focal, low-grade, organ-confined prostate cancer (CaP). Additionally, LITT is compatible with multi-parametric magnetic resonance imaging (MP-MRI) which in turn enables (1) high resolution, accurate localization of ablation zones on in vivo MP-MRI prior to LITT, and (2) real-time monitoring of temperature changes in vivo via MR thermometry during LITT. In spite of rapidly increasing interest in the use of LITT for treating low grade, focal CaP, very little is known about treatment-related changes following LITT. There is thus a clear need for studying post-LITT changes via MP-MRI and consequently to attempt to (1) quantitatively identify MP-MRI markers predictive of favorable treatment response and longer term patient outcome, and (2) identify which MP-MRI markers are most sensitive to post-LITT changes in the prostate. In this work, we present the first attempt at examining focal treatment-related changes on a per-voxel basis (high resolution) via quantitative evaluation of MR parameters pre- and post-LITT. A retrospective cohort of MP-MRI data comprising both pre- and post-LITT T2-weighted (T2w) and diffusion-weighted (DWI) acquisitions was considered, where DWI MRI yielded an Apparent Diffusion Co-efficient (ADC) map. A spatially constrained affine registration scheme was implemented to first bring T2w and ADC images into alignment within each of the pre- and post-LITT acquisitions, following which the pre- and post-LITT acquisitions were aligned. Pre- and post-LITT MR parameters (T2w intensity, ADC value) were then standardized to a uniform scale (to correct for intensity drift) and then quantified via the raw intensity values as well as via texture features derived from T2w MRI. In order to quantify imaging changes as a result of LITT, absolute differences were calculated between the normalized pre- and post-LITT MRI parameters. Quantitatively combining the ADC and T2w MRI parameters enabled construction of an integrated MP-MRI difference map that was highly indicative of changes specific to the LITT ablation zone. Preliminary quantitative comparison of the changes in different MR parameters indicated that T2w texture may be highly sensitive as well as specific in identifying changes within the ablation zone pre- and post-LITT. Visual evaluation of the differences in T2w texture features pre- and post-LITT also appeared to provide an indication of LITT-related effects such as edema. Our preliminary results thus indicate great potential for non-invasive MP-MRI imaging markers for determining focal treatment related changes, and hence long- and short-term patient outcome. © 2013 SPIE.\n
\n\n\n\n \n\n \n \n \n \n \n Discriminatively weighted multi-scale Local Binary Patterns: Applications in prostate cancer diagnosis on T2W MRI.\n \n \n \n\n\n \n Wang, H.; Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n 2013.\n \n\n\n\n
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\n\n\n@misc{Wang2013,\n abstract = {In this paper, we present discriminatively weighted Local Binary Patterns (DWLBP), a new similarity metric to match Multi-scale LBP (MsLBP) in Hamming space. While MsLBP is widely used in image processing on account of its extremely fast bitwise operations on modern CPU, identifying a good metric that measures the dissimilarity of MsLBP remains an open problem. The Hamming score is typically computed at each individual scale and the scores across scales are summed up. This approach however often results in underestimating salient patterns. In this paper we seek to learn a vector obtained by optimally weighing the contribution of each individual scale when performing MsLBP based matching. Inspired by supervised learning, our methodology aims to learn the multi-scale, weight vector by minimizing the Hamming scores between positive class samples and jointly maximizing the scores between positive and negative class samples. This objective function leads to a convex formulation with equality and inequality constraints, which can then be solved via the interior-point optimization method. In this paper we evaluate the efficacy of the DWLBP scheme in detecting prostate cancer from T2w MRI and demonstrate that the approach statistically significantly outperforms MsLBP. © 2013 IEEE.},\n author = {H. Wang and S.E. Viswanath and A. Madabhushi},\n doi = {10.1109/ISBI.2013.6556496},\n isbn = {9781467364546},\n issn = {19457928},\n journal = {Proceedings - International Symposium on Biomedical Imaging},\n keywords = {Image Processing,Local Binary Patterns,MRI,Prostate Cancer,multi-scale},\n title = {Discriminatively weighted multi-scale Local Binary Patterns: Applications in prostate cancer diagnosis on T2W MRI},\n year = {2013},\n}\n\n\n In this paper, we present discriminatively weighted Local Binary Patterns (DWLBP), a new similarity metric to match Multi-scale LBP (MsLBP) in Hamming space. While MsLBP is widely used in image processing on account of its extremely fast bitwise operations on modern CPU, identifying a good metric that measures the dissimilarity of MsLBP remains an open problem. The Hamming score is typically computed at each individual scale and the scores across scales are summed up. This approach however often results in underestimating salient patterns. In this paper we seek to learn a vector obtained by optimally weighing the contribution of each individual scale when performing MsLBP based matching. Inspired by supervised learning, our methodology aims to learn the multi-scale, weight vector by minimizing the Hamming scores between positive class samples and jointly maximizing the scores between positive and negative class samples. This objective function leads to a convex formulation with equality and inequality constraints, which can then be solved via the interior-point optimization method. In this paper we evaluate the efficacy of the DWLBP scheme in detecting prostate cancer from T2w MRI and demonstrate that the approach statistically significantly outperforms MsLBP. © 2013 IEEE.\n
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\n \n \n\n \n \n \n \n \n Consensus embedding: Theory, algorithms and application to segmentation and classification of biomedical data.\n \n \n \n\n\n \n Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n BMC Bioinformatics, 13. 2012.\n \n\n\n\n
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\n\n\n@article{Viswanath2012,\n abstract = {Background: Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding (generated via DR schemes such as Principal Component Analysis, Graph Embedding, or Locally Linear Embedding). Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme.Results: Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered.Conclusions: We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis. © 2012 Viswanath and Madabhushi; licensee BioMed Central Ltd.},\n author = {S.E. Viswanath and A. Madabhushi},\n doi = {10.1186/1471-2105-13-26},\n issn = {14712105},\n issue = {1},\n journal = {BMC Bioinformatics},\n title = {Consensus embedding: Theory, algorithms and application to segmentation and classification of biomedical data},\n volume = {13},\n year = {2012},\n}\n\n\n Background: Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding (generated via DR schemes such as Principal Component Analysis, Graph Embedding, or Locally Linear Embedding). Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme.Results: Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered.Conclusions: We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis. © 2012 Viswanath and Madabhushi; licensee BioMed Central Ltd.\n
\n\n\n\n \n\n \n \n \n \n \n Multimodal wavelet embedding representation for data combination (MaWERiC): Integrating magnetic resonance imaging and spectroscopy for prostate cancer detection.\n \n \n \n\n\n \n Tiwari, P.; Viswanath, S.; Kurhanewicz, J.; Sridhar, A.; and Madabhushi, A.\n\n\n \n\n\n\n NMR in Biomedicine, 25. 2012.\n \n\n\n\n
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\n\n\n@article{Tiwari2012,\n abstract = {Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T <inf>2</inf> weighted MRI (T <inf>2</inf>w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a novel computerized decision support system called multimodal wavelet embedding representation for data combination (MaWERiC) that employs, (i) wavelet theory to extract 171 Haar wavelet features from MRS and 54 Gabor features from T <inf>2</inf>w MRI, (ii) dimensionality reduction to individually project wavelet features from MRS and T <inf>2</inf>w MRI into a common reduced Eigen vector space, and (iii), a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 T in vivo MRI and MRS. A total of 36 1.5T endorectal in vivo T <inf>2</inf>w MRI and MRS patient studies were evaluated per voxel by MaWERiC using a three-fold cross validation approach over 25 iterations. Ground truth for evaluation of results was obtained by an expert radiologist annotations of prostate cancer on a per voxel basis who compared each MRI section with corresponding ex vivo wholemount histology sections with the disease extent mapped out on histology. Results suggest that MaWERiC based MRS T <inf>2</inf>w meta-classifier (mean AUC, μ=0.89±0.02) significantly outperformed (i) a T <inf>2</inf>w MRI (using wavelet texture features) classifier (μ=0.55±0.02), (ii) a MRS (using metabolite ratios) classifier (μ=0.77±0.03), (iii) a decision fusion classifier obtained by combining individual T <inf>2</inf>w MRI and MRS classifier outputs (μ=0.85±0.03), and (iv) a data combination method involving a combination of metabolic MRS and MR signal intensity features (μ=0.66±0.02). © 2011 John Wiley & Sons, Ltd.},\n author = {P. Tiwari and S.E. Viswanath and J. Kurhanewicz and A. Sridhar and A. Madabhushi},\n doi = {10.1002/nbm.1777},\n issn = {09523480},\n issue = {4},\n journal = {NMR in Biomedicine},\n keywords = {Gabor texture features,Haar wavelets,Magnetic resonance imaging,Magnetic resonance spectroscopy,Multimodal integration,PCA, random forest classifier,Prostate cancer},\n title = {Multimodal wavelet embedding representation for data combination (MaWERiC): Integrating magnetic resonance imaging and spectroscopy for prostate cancer detection},\n volume = {25},\n year = {2012},\n}\n\n\n Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T 2 weighted MRI (T 2 w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a novel computerized decision support system called multimodal wavelet embedding representation for data combination (MaWERiC) that employs, (i) wavelet theory to extract 171 Haar wavelet features from MRS and 54 Gabor features from T 2 w MRI, (ii) dimensionality reduction to individually project wavelet features from MRS and T 2 w MRI into a common reduced Eigen vector space, and (iii), a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 T in vivo MRI and MRS. A total of 36 1.5T endorectal in vivo T 2 w MRI and MRS patient studies were evaluated per voxel by MaWERiC using a three-fold cross validation approach over 25 iterations. Ground truth for evaluation of results was obtained by an expert radiologist annotations of prostate cancer on a per voxel basis who compared each MRI section with corresponding ex vivo wholemount histology sections with the disease extent mapped out on histology. Results suggest that MaWERiC based MRS T 2 w meta-classifier (mean AUC, μ=0.89±0.02) significantly outperformed (i) a T 2 w MRI (using wavelet texture features) classifier (μ=0.55±0.02), (ii) a MRS (using metabolite ratios) classifier (μ=0.77±0.03), (iii) a decision fusion classifier obtained by combining individual T 2 w MRI and MRS classifier outputs (μ=0.85±0.03), and (iv) a data combination method involving a combination of metabolic MRS and MR signal intensity features (μ=0.66±0.02). © 2011 John Wiley & Sons, Ltd.\n
\n\n\n\n \n\n \n \n \n \n \n Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2-weighted MR imagery.\n \n \n \n\n\n \n Viswanath, S.; Bloch, N.; Chappelow, J.; Toth, R.; Rofsky, N.; Genega, E.; Lenkinski, R.; and Madabhushi, A.\n\n\n \n\n\n\n Journal of Magnetic Resonance Imaging, 36. 2012.\n \n\n\n\n
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\n\n\n@article{Viswanath2012,\n abstract = {Purpose: To identify and evaluate textural quantitative imaging signatures (QISes) for tumors occurring within the central gland (CG) and peripheral zone (PZ) of the prostate, respectively, as seen on in vivo 3 Tesla (T) endorectal T2-weighted (T2w) MRI. Materials and Methods: This study used 22 preoperative prostate MRI data sets (16 PZ, 6 CG) acquired from men with confirmed prostate cancer (CaP) and scheduled for radical prostatectomy (RP). The prostate region-of-interest (ROI) was automatically delineated on T2w MRI, following which it was corrected for intensity-based acquisition artifacts. An expert pathologist manually delineated the dominant tumor regions on ex vivo sectioned and stained RP specimens as well as identified each of the studies as either a CG or PZ CaP. A nonlinear registration scheme was used to spatially align and then map CaP extent from the ex vivo RP sections onto the corresponding MRI slices. A total of 110 texture features were then extracted on a per-voxel basis from all T2w MRI data sets. An information theoretic feature selection procedure was then applied to identify QISes comprising T2w MRI textural features specific to CG and PZ CaP, respectively. The QISes for CG and PZ CaP were evaluated by means of Quadratic Discriminant Analysis (QDA) on a per-voxel basis against the ground truth for CaP on T2w MRI, mapped from corresponding histology. Results: The QDA classifier yielded an area under the Receiver Operating characteristic curve of 0.86 for the CG CaP studies, and 0.73 for the PZ CaP studies over 25 runs of randomized three-fold cross-validation. By comparison, the accuracy of the QDA classifier was significantly lower when (a) using all 110 texture features (with no feature selection applied), as well as (b) a randomly selected combination of texture features. Conclusion: CG and PZ prostate cancers have significantly differing textural quantitative imaging signatures on T2w endorectal in vivo MRI. © 2012 Wiley Periodicals, Inc.},\n author = {S.E. Viswanath and N.B. Bloch and J.C. Chappelow and R. Toth and N.M. Rofsky and E.M. Genega and R.E. Lenkinski and A. Madabhushi},\n doi = {10.1002/jmri.23618},\n issn = {10531807},\n issue = {1},\n journal = {Journal of Magnetic Resonance Imaging},\n keywords = {T2-weighted,central gland,classification,magnetic resonance imaging,peripheral zone,prostate cancer,quantitative imaging signatures,texture analysis},\n title = {Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2-weighted MR imagery},\n volume = {36},\n year = {2012},\n}\n\n\n Purpose: To identify and evaluate textural quantitative imaging signatures (QISes) for tumors occurring within the central gland (CG) and peripheral zone (PZ) of the prostate, respectively, as seen on in vivo 3 Tesla (T) endorectal T2-weighted (T2w) MRI. Materials and Methods: This study used 22 preoperative prostate MRI data sets (16 PZ, 6 CG) acquired from men with confirmed prostate cancer (CaP) and scheduled for radical prostatectomy (RP). The prostate region-of-interest (ROI) was automatically delineated on T2w MRI, following which it was corrected for intensity-based acquisition artifacts. An expert pathologist manually delineated the dominant tumor regions on ex vivo sectioned and stained RP specimens as well as identified each of the studies as either a CG or PZ CaP. A nonlinear registration scheme was used to spatially align and then map CaP extent from the ex vivo RP sections onto the corresponding MRI slices. A total of 110 texture features were then extracted on a per-voxel basis from all T2w MRI data sets. An information theoretic feature selection procedure was then applied to identify QISes comprising T2w MRI textural features specific to CG and PZ CaP, respectively. The QISes for CG and PZ CaP were evaluated by means of Quadratic Discriminant Analysis (QDA) on a per-voxel basis against the ground truth for CaP on T2w MRI, mapped from corresponding histology. Results: The QDA classifier yielded an area under the Receiver Operating characteristic curve of 0.86 for the CG CaP studies, and 0.73 for the PZ CaP studies over 25 runs of randomized three-fold cross-validation. By comparison, the accuracy of the QDA classifier was significantly lower when (a) using all 110 texture features (with no feature selection applied), as well as (b) a randomly selected combination of texture features. Conclusion: CG and PZ prostate cancers have significantly differing textural quantitative imaging signatures on T2w endorectal in vivo MRI. © 2012 Wiley Periodicals, Inc.\n
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\n \n \n\n \n \n \n \n \n Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): Detecting prostate cancer on multi-parametric MRI.\n \n \n \n\n\n \n Viswanath, S.; Bloch, B.; Chappelow, J.; Patel, P.; Rofsky, N.; Lenkinski, R.; Genega, E.; and Madabhushi, A.\n\n\n \n\n\n\n 2011.\n \n\n\n\n
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\n\n\n@misc{Viswanath2011,\n abstract = {Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67). © 2011 SPIE.},\n author = {S.E. Viswanath and B.N. Bloch and J.C. Chappelow and P. Patel and N.M. Rofsky and R.E. Lenkinski and E.M. Genega and A. Madabhushi},\n doi = {10.1117/12.878312},\n isbn = {9780819485052},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {3 Tesla,CAD,DCE-MRI,DWI-MRI,T2w MRI,ensemble embedding,multi-modal integration,multi-parametric,multi-protocol,non-rigid registration,probabilistic boosting trees,prostate cancer,supervised learning},\n title = {Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): Detecting prostate cancer on multi-parametric MRI},\n volume = {7963},\n year = {2011},\n}\n\n\n Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67). © 2011 SPIE.\n
\n\n\n\n \n\n \n \n \n \n \n Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI.\n \n \n \n\n\n \n Viswanath, S.; Palumbo, D.; Chappelow, J.; Patel, P.; Bloch, B.; Rofsky, N.; Lenkinski, R.; Genega, E.; and Madabhushi, A.\n\n\n \n\n\n\n 2011.\n \n\n\n\n
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\n\n\n@misc{Viswanath2011,\n abstract = {In magnetic resonance imaging (MRI), intensity inhomogeneity refers to an acquisition artifact which introduces a non-linear variation in the signal intensities within the image. Intensity inhomogeneity is known to significantly affect computerized analysis of MRI data (such as automated segmentation or classification procedures), hence requiring the application of bias field correction (BFC) algorithms to account for this artifact. Quantitative evaluation of BFC schemes is typically performed using generalized intensity-based measures (percent coefficient of variation, %CV) or information-theoretic measures (entropy). While some investigators have previously empirically compared BFC schemes in the context of different domains (using changes in %CV and entropy to quantify improvements), no consensus has emerged as to the best BFC scheme for any given application. The motivation for this work is that the choice of a BFC scheme for a given application should be dictated by application-specific measures rather than ad hoc measures such as entropy and %CV. In this paper, we have attempted to address the problem of determining an optimal BFC algorithm in the context of a computer-aided diagnosis (CAD) scheme for prostate cancer (CaP) detection from T2-weighted (T2w) MRI. One goal of this work is to identify a BFC algorithm that will maximize the CaP classification accuracy (measured in terms of the area under the ROC curve or AUC). A secondary aim of our work is to determine whether measures such as %CV and entropy are correlated with a classifier-based objective measure (AUC). Determining the presence or absence of these correlations is important to understand whether domain independent BFC performance measures such as %CV, entropy should be used to identify the optimal BFC scheme for any given application. In order to answer these questions, we quantitatively compared 3 different popular BFC algorithms on a cohort of 10 clinical 3 Tesla prostate T2w MRI datasets (comprising 39 2D MRI slices): N3, PABIC, and the method of Cohen et al. Results of BFC via each of the algorithms was evaluated in terms of %CV, entropy, as well as classifier AUC for CaP detection from T2w MRI. The CaP classifier was trained and evaluated on a per-pixel basis using annotations of CaP obtained via registration of T2w MRI and ex vivo whole-mount histology sections. Our results revealed that different BFC schemes resulted in a maximization of different performance measures, that is, the BFC scheme identified by minimization of %CV and entropy was not the one that maximized AUC as well. Moreover, existing BFC evaluation measures (%CV, entropy) did not correlate with AUC (application-based evaluation), but did correlate with each other, suggesting that domain-specific performance measures should be considered in making a decision regarding choice of appropriate BFC scheme. Our results also revealed that N3 provided the best correction of bias field artifacts in prostate MRI data, when the goal was to identify prostate cancer. © 2011 SPIE.},\n author = {S.E. Viswanath and D. Palumbo and J.C. Chappelow and P. Patel and B.N. Bloch and N.M. Rofsky and R.E. Lenkinski and E.M. Genega and A. Madabhushi},\n doi = {10.1117/12.878813},\n isbn = {9780819485052},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {T2w MRI,bias field correction,classification,intensity inhomogeneity,prostate cancer},\n title = {Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI},\n volume = {7963},\n year = {2011},\n}\n\n\n In magnetic resonance imaging (MRI), intensity inhomogeneity refers to an acquisition artifact which introduces a non-linear variation in the signal intensities within the image. Intensity inhomogeneity is known to significantly affect computerized analysis of MRI data (such as automated segmentation or classification procedures), hence requiring the application of bias field correction (BFC) algorithms to account for this artifact. Quantitative evaluation of BFC schemes is typically performed using generalized intensity-based measures (percent coefficient of variation, %CV) or information-theoretic measures (entropy). While some investigators have previously empirically compared BFC schemes in the context of different domains (using changes in %CV and entropy to quantify improvements), no consensus has emerged as to the best BFC scheme for any given application. The motivation for this work is that the choice of a BFC scheme for a given application should be dictated by application-specific measures rather than ad hoc measures such as entropy and %CV. In this paper, we have attempted to address the problem of determining an optimal BFC algorithm in the context of a computer-aided diagnosis (CAD) scheme for prostate cancer (CaP) detection from T2-weighted (T2w) MRI. One goal of this work is to identify a BFC algorithm that will maximize the CaP classification accuracy (measured in terms of the area under the ROC curve or AUC). A secondary aim of our work is to determine whether measures such as %CV and entropy are correlated with a classifier-based objective measure (AUC). Determining the presence or absence of these correlations is important to understand whether domain independent BFC performance measures such as %CV, entropy should be used to identify the optimal BFC scheme for any given application. In order to answer these questions, we quantitatively compared 3 different popular BFC algorithms on a cohort of 10 clinical 3 Tesla prostate T2w MRI datasets (comprising 39 2D MRI slices): N3, PABIC, and the method of Cohen et al. Results of BFC via each of the algorithms was evaluated in terms of %CV, entropy, as well as classifier AUC for CaP detection from T2w MRI. The CaP classifier was trained and evaluated on a per-pixel basis using annotations of CaP obtained via registration of T2w MRI and ex vivo whole-mount histology sections. Our results revealed that different BFC schemes resulted in a maximization of different performance measures, that is, the BFC scheme identified by minimization of %CV and entropy was not the one that maximized AUC as well. Moreover, existing BFC evaluation measures (%CV, entropy) did not correlate with AUC (application-based evaluation), but did correlate with each other, suggesting that domain-specific performance measures should be considered in making a decision regarding choice of appropriate BFC scheme. Our results also revealed that N3 provided the best correction of bias field artifacts in prostate MRI data, when the goal was to identify prostate cancer. © 2011 SPIE.\n
\n\n\n\n \n\n \n \n \n \n \n A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma.\n \n \n \n\n\n \n Lai, Y.; Viswanath, S.; Baccon, J.; Ellison, D.; Judkins, A.; and Madabhushi, A.\n\n\n \n\n\n\n 2011.\n \n\n\n\n
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\n\n\n@misc{Lai2011,\n abstract = {Medulloblastoma (MB) is the most common brain tumor in children. There are four distinct subtypes of MB, but patients with anaplastic/large cell have the worst prognosis. Since the morbidity is highly correlated with treatment for MB, the ability to distinguish aggressive (such as anaplastic/large cell) MB is crucial. We present a scheme that leverages quantitative image texture features (Haar, Haralick, and Laws) and classifier ensembles (random forests) to automatically classify histological images from MB resection as being anaplastic/large cell or non-anaplastic/large cell. Preliminary results for our scheme when applied to patch-based classification of MB specimens yield an AUC of 0.91. © 2011 IEEE.},\n author = {Y. Lai and S.E. Viswanath and J. Baccon and D.W. Ellison and A.R. Judkins and A. Madabhushi},\n doi = {10.1109/NEBC.2011.5778641},\n isbn = {9781612848273},\n journal = {2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011},\n title = {A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma},\n year = {2011},\n}\n\n\n Medulloblastoma (MB) is the most common brain tumor in children. There are four distinct subtypes of MB, but patients with anaplastic/large cell have the worst prognosis. Since the morbidity is highly correlated with treatment for MB, the ability to distinguish aggressive (such as anaplastic/large cell) MB is crucial. We present a scheme that leverages quantitative image texture features (Haar, Haralick, and Laws) and classifier ensembles (random forests) to automatically classify histological images from MB resection as being anaplastic/large cell or non-anaplastic/large cell. Preliminary results for our scheme when applied to patch-based classification of MB specimens yield an AUC of 0.91. © 2011 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n Weighted combination of multi-parametric MR imaging markers for evaluating radiation therapy related changes in the prostate.\n \n \n \n\n\n \n Tiwari, P.; Viswanath, S.; Kurhanewicz, J.; and Madabhushi, A.\n\n\n \n\n\n\n 2011.\n \n\n\n\n
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\n\n\n@misc{Tiwari2011,\n abstract = {Recently, multi-parametric (MP) Magnetic Resonance (MR) Imaging (T2-weighted, MR Spectroscopy (MRS), Diffusion-weighted (DWI)) has shown great potential for evaluating the early effects of radiotherapy (RT) in the prostate. In this work we present a framework for quantitatively combining MP-MRI markers in order to assess RT changes on a voxel-by-voxel basis. The suite of segmentation, registration, feature extraction, and classifier tools presented in this work will allow for identification of (a) residual disease, and (b) new foci of cancer (local recurrence) within the prostate. Our scheme involves, (a) simultaneously evaluating differences in pre-, post-RT MR imaging markers, and (b) intelligently integrating and weighting the imaging marker changes obtained in (a) to generate a combined MP-MRI difference map that can better quantify treatment specific changes in the prostate. We demonstrate the applicability of our scheme in studying intensity-modulated radiation therapy (IMRT)-related changes for a cohort of 14 MP (T2w, MRS, DWI) prostate MRI patient datasets. In the first step, the different MRI protocols from pre- and post-IMRT MRI scans are affinely registered (accounting for gland shrinkage), followed by automated segmentation of the prostate capsule using an active shape model. Individual imaging marker difference maps are generated by calculating the differences of textural, metabolic, and functional MRI marker attributes, pre- and post-RT, on a per-voxel basis. These difference maps are then combined via an intelligent optimization scheme to generate a combined weighted difference map, where higher difference values on the map signify larger change (new foci of cancer), and low difference values signify no/small change post-RT. In the absence of histological ground truth (surgical or biopsy), radiologist delineated CaP extent on pre-, and post-RT MRI was employed as the ground truth for evaluating the accuracy of our scheme in successfully identifying MP-MRI related disease changes post-RT. A mean area under the receiver operating curve (AUC) of 73.2% was obtained via the weighted MP-MRI map, when evaluated against expert delineated CaP extent on pre-, post-RT MRI. The difference maps corresponding to the individual structural (T2w intensities), functional (ADC intensities) and metabolic (choline, creatine) markers yielded a corresponding mean AUC of 54.4%, 68.6% and 70.8%. © 2011 Springer-Verlag.},\n author = {P. Tiwari and S.E. Viswanath and J. Kurhanewicz and A. Madabhushi},\n doi = {10.1007/978-3-642-23944-1_9},\n isbn = {9783642239434},\n issn = {03029743},\n journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n title = {Weighted combination of multi-parametric MR imaging markers for evaluating radiation therapy related changes in the prostate},\n volume = {6963 LNCS},\n year = {2011},\n}\n\n\n Recently, multi-parametric (MP) Magnetic Resonance (MR) Imaging (T2-weighted, MR Spectroscopy (MRS), Diffusion-weighted (DWI)) has shown great potential for evaluating the early effects of radiotherapy (RT) in the prostate. In this work we present a framework for quantitatively combining MP-MRI markers in order to assess RT changes on a voxel-by-voxel basis. The suite of segmentation, registration, feature extraction, and classifier tools presented in this work will allow for identification of (a) residual disease, and (b) new foci of cancer (local recurrence) within the prostate. Our scheme involves, (a) simultaneously evaluating differences in pre-, post-RT MR imaging markers, and (b) intelligently integrating and weighting the imaging marker changes obtained in (a) to generate a combined MP-MRI difference map that can better quantify treatment specific changes in the prostate. We demonstrate the applicability of our scheme in studying intensity-modulated radiation therapy (IMRT)-related changes for a cohort of 14 MP (T2w, MRS, DWI) prostate MRI patient datasets. In the first step, the different MRI protocols from pre- and post-IMRT MRI scans are affinely registered (accounting for gland shrinkage), followed by automated segmentation of the prostate capsule using an active shape model. Individual imaging marker difference maps are generated by calculating the differences of textural, metabolic, and functional MRI marker attributes, pre- and post-RT, on a per-voxel basis. These difference maps are then combined via an intelligent optimization scheme to generate a combined weighted difference map, where higher difference values on the map signify larger change (new foci of cancer), and low difference values signify no/small change post-RT. In the absence of histological ground truth (surgical or biopsy), radiologist delineated CaP extent on pre-, and post-RT MRI was employed as the ground truth for evaluating the accuracy of our scheme in successfully identifying MP-MRI related disease changes post-RT. A mean area under the receiver operating curve (AUC) of 73.2% was obtained via the weighted MP-MRI map, when evaluated against expert delineated CaP extent on pre-, post-RT MRI. The difference maps corresponding to the individual structural (T2w intensities), functional (ADC intensities) and metabolic (choline, creatine) markers yielded a corresponding mean AUC of 54.4%, 68.6% and 70.8%. © 2011 Springer-Verlag.\n
\n\n\n\n \n\n \n \n \n \n \n CADOn©: An integrated toolkit for evaluating radiation therapy related changes in the prostate using multiparametric MRI.\n \n \n \n\n\n \n Viswanath, S.; Tiwari, P.; Chappelow, J.; Toth, R.; Kurhanewicz, J.; and Madabhushi, A.\n\n\n \n\n\n\n 2011.\n \n\n\n\n
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\n\n\n@misc{Viswanath2011,\n abstract = {The use of multi-parametric Magnetic Resonance Imaging (T2-weighted, MR Spectroscopy (MRS), Diffusion-weighted (DWI)) has recently shown great promise for diagnosing and staging prostate cancer (CaP) in vivo. Such imaging has also been utilized for evaluating the early effects of radiotherapy (RT) (e.g. intensity-modulated radiation therapy (IMRT), proton beam therapy, brachytherapy) in the prostate with the overarching goal being to successfully predict short- and long-term patient outcome. Qualitative examination of post-RT changes in the prostate using MRI is subject to high inter- and intra-observer variability. Consequently, there is a clear need for quantitative image segmentation, registration, and classification tools for assessing RT changes via multi-parametric MRI to identify (a) residual disease, and (b) new foci of cancer (local recurrence) within the prostate. In this paper, we present a computerized image segmentation, registration, and classification toolkit called CADOnc , and leverage it for evaluating (a) spatial extent of disease pre-RT, and (b) post-RT related changes within the prostate. We demonstrate the applicability of CADOnc in studying IMRT-related changes using a cohort of 7 multi-parametric (T2w, MRS, DWI) prostate MRI patient datasets. First, the different MRI protocols from pre- and post-IMRT MRI scans are affinely registered (accounting for gland shrinkage), followed by automated segmentation of the prostate capsule using an active shape model. A number of feature extraction schemes are then applied to extract multiple textural, metabolic, and functional MRI attributes on a per-voxel basis. An AUC of 0.7132 was achieved for automated detection of CaP on pre-IMRT MRI (via integration of T2w, DWI, MRS features); evaluated on a per-voxel basis against radiologist-derived annotations. CADOnc also successfully identified a total of 40 out of 46 areas where disease-related changes (both absence and recurrence) occurred post-IMRT, based on changes in the expression of quantitative MR imaging biomarkers. CADOnc thus provides an integrated platform of quantitative analysis tools to evaluate treatment response in vivo, based on multi-parametric MRI data. © 2011 IEEE.},\n author = {S.E. Viswanath and P. Tiwari and J.C. Chappelow and R. Toth and J. Kurhanewicz and A. Madabhushi},\n doi = {10.1109/ISBI.2011.5872825},\n isbn = {9781424441280},\n issn = {19457928},\n journal = {Proceedings - International Symposium on Biomedical Imaging},\n title = {CADOn©: An integrated toolkit for evaluating radiation therapy related changes in the prostate using multiparametric MRI},\n year = {2011},\n}\n\n\n The use of multi-parametric Magnetic Resonance Imaging (T2-weighted, MR Spectroscopy (MRS), Diffusion-weighted (DWI)) has recently shown great promise for diagnosing and staging prostate cancer (CaP) in vivo. Such imaging has also been utilized for evaluating the early effects of radiotherapy (RT) (e.g. intensity-modulated radiation therapy (IMRT), proton beam therapy, brachytherapy) in the prostate with the overarching goal being to successfully predict short- and long-term patient outcome. Qualitative examination of post-RT changes in the prostate using MRI is subject to high inter- and intra-observer variability. Consequently, there is a clear need for quantitative image segmentation, registration, and classification tools for assessing RT changes via multi-parametric MRI to identify (a) residual disease, and (b) new foci of cancer (local recurrence) within the prostate. In this paper, we present a computerized image segmentation, registration, and classification toolkit called CADOnc , and leverage it for evaluating (a) spatial extent of disease pre-RT, and (b) post-RT related changes within the prostate. We demonstrate the applicability of CADOnc in studying IMRT-related changes using a cohort of 7 multi-parametric (T2w, MRS, DWI) prostate MRI patient datasets. First, the different MRI protocols from pre- and post-IMRT MRI scans are affinely registered (accounting for gland shrinkage), followed by automated segmentation of the prostate capsule using an active shape model. A number of feature extraction schemes are then applied to extract multiple textural, metabolic, and functional MRI attributes on a per-voxel basis. An AUC of 0.7132 was achieved for automated detection of CaP on pre-IMRT MRI (via integration of T2w, DWI, MRS features); evaluated on a per-voxel basis against radiologist-derived annotations. CADOnc also successfully identified a total of 40 out of 46 areas where disease-related changes (both absence and recurrence) occurred post-IMRT, based on changes in the expression of quantitative MR imaging biomarkers. CADOnc thus provides an integrated platform of quantitative analysis tools to evaluate treatment response in vivo, based on multi-parametric MRI data. © 2011 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n Interplay between bias field correction, intensity standardization, and noise filtering for T2-weighted MRI.\n \n \n \n\n\n \n Palumbo, D.; Yee, B.; O'Dea, P.; Leedy, S.; Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n 2011.\n \n\n\n\n
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\n\n\n@misc{Palumbo2011,\n abstract = {Magnetic Resonance Imaging (MRI) is known to be significantly affected by a number of acquisition artifacts, such as intensity non-standardness, bias field, and Gaussian noise. These artifacts degrade MR image quality significantly, obfuscating anatomical and physiological detail and hence need to be corrected for to facilitate application of computerized analysis techniques such as segmentation, registration, and classification. Specifically, algorithms are required to correct for bias field (intensity inhomogeneity), intensity non-standardness (drift in tissue intensities across patient acquisitions), and Gaussian noise, an artifact that significantly affects and blurs tissue boundaries (resulting in poor gradients). While clearly one needs to correct for all these artifacts, the exact sequence in which all three operations need to be applied in order to maximize MR image quality has not been explored. In this paper, we empirically evaluate the interplay between distinct algorithms for bias field correction (BFC), intensity standardization (IS), and noise filtering (NF) to study the effect of these operations on image quality in the context of 3 Tesla T2-weighted (T2w) prostate MRI. 7 different sequences comprising combinations of BFC, IS, and NF were quantitatively evaluated in terms of the percent coefficient of variation (%CV), a statistic which attempts to quantify the intensity inhomogeneity within a region of interest (prostate). The different combinations were also independently evaluated in the context of a classifier scheme for detection of prostate cancer on high resolution in vivo T2w prostate MRI. A secondary contribution of this work is a novel evaluation measure for quantifying the level of intensity non-standardness, called difference of modes (DoM). Experimental evaluation of the different sequences of operations across 22 patient datasets revealed that the sequence of BFC, followed by NF, and IS provided the best image quality in terms of %CV as well as classifier accuracy. The DoM measure was able to accurately capture the level of intensity non-standardness present in the images resulting from the different sequences of operations. © 2011 IEEE.},\n author = {D. Palumbo and B. Yee and P. O'Dea and S. Leedy and S.E. Viswanath and A. Madabhushi},\n doi = {10.1109/IEMBS.2011.6091258},\n isbn = {9781424441211},\n issn = {1557170X},\n journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},\n title = {Interplay between bias field correction, intensity standardization, and noise filtering for T2-weighted MRI},\n year = {2011},\n}\n\n\n Magnetic Resonance Imaging (MRI) is known to be significantly affected by a number of acquisition artifacts, such as intensity non-standardness, bias field, and Gaussian noise. These artifacts degrade MR image quality significantly, obfuscating anatomical and physiological detail and hence need to be corrected for to facilitate application of computerized analysis techniques such as segmentation, registration, and classification. Specifically, algorithms are required to correct for bias field (intensity inhomogeneity), intensity non-standardness (drift in tissue intensities across patient acquisitions), and Gaussian noise, an artifact that significantly affects and blurs tissue boundaries (resulting in poor gradients). While clearly one needs to correct for all these artifacts, the exact sequence in which all three operations need to be applied in order to maximize MR image quality has not been explored. In this paper, we empirically evaluate the interplay between distinct algorithms for bias field correction (BFC), intensity standardization (IS), and noise filtering (NF) to study the effect of these operations on image quality in the context of 3 Tesla T2-weighted (T2w) prostate MRI. 7 different sequences comprising combinations of BFC, IS, and NF were quantitatively evaluated in terms of the percent coefficient of variation (%CV), a statistic which attempts to quantify the intensity inhomogeneity within a region of interest (prostate). The different combinations were also independently evaluated in the context of a classifier scheme for detection of prostate cancer on high resolution in vivo T2w prostate MRI. A secondary contribution of this work is a novel evaluation measure for quantifying the level of intensity non-standardness, called difference of modes (DoM). Experimental evaluation of the different sequences of operations across 22 patient datasets revealed that the sequence of BFC, followed by NF, and IS provided the best image quality in terms of %CV as well as classifier accuracy. The DoM measure was able to accurately capture the level of intensity non-standardness present in the images resulting from the different sequences of operations. © 2011 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data.\n \n \n \n\n\n \n Tiwari, P.; Viswanath, S.; Lee, G.; and Madabhushi, A.\n\n\n \n\n\n\n 2011.\n \n\n\n\n
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\n\n\n@misc{Tiwari2011,\n abstract = {With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data available for disease diagnosis and prognosis, there is a need for quantitative tools to combine such varied channels of information, especially imaging and non-imaging data (e.g. spectroscopy, proteomics). The major problem in such quantitative data integration lies in reconciling the large spread in the range of dimensionalities and scales across the different modalities. The primary goal of quantitative data integration is to build combined meta-classifiers; however these efforts are thwarted by challenges in (1) homogeneous representation of the data channels, (2) fusing the attributes to construct an integrated feature vector, and (3) the choice of learning strategy for training the integrated classifier. In this paper, we seek to (a) define the characteristics that guide the 4 independent methods for quantitative data fusion that use the idea of a meta-space for building integrated multi-modal, multi-scale meta-classifiers, and (b) attempt to understand the key components which allowed each method to succeed. These methods include (1) Generalized Embedding Concatenation (GEC), (2) Consensus Embedding (CE), (3) Semi-Supervised Multi-Kernel Graph Embedding (SeSMiK), and (4) Boosted Embedding Combination (BEC). In order to evaluate the optimal scheme for fusing imaging and non-imaging data, we compared these 4 schemes for the problems of combining (a) multi-parametric MRI with spectroscopy for prostate cancer (CaP) diagnosis in vivo, and (b) histological image with proteomic signatures (obtained via mass spectrometry) for predicting prognosis in CaP patients. The kernel combination approach (SeSMiK) marginally outperformed the embedding combination schemes. Additionally, intelligent weighting of the data channels (based on their relative importance) appeared to outperform unweighted strategies. All 4 strategies easily outperformed a nave decision fusion approach, suggesting that data integration methods will play an important role in the rapidly emerging field of integrated diagnostics and personalized healthcare. © 2011 IEEE.},\n author = {P. Tiwari and S.E. Viswanath and G. Lee and A. Madabhushi},\n doi = {10.1109/ISBI.2011.5872379},\n isbn = {9781424441280},\n issn = {19457928},\n journal = {Proceedings - International Symposium on Biomedical Imaging},\n keywords = {Consensus Embedding,GFF,Kernel combination,SeSMiK,data fusion,prostate cancer},\n title = {Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data},\n year = {2011},\n}\n\n\n With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data available for disease diagnosis and prognosis, there is a need for quantitative tools to combine such varied channels of information, especially imaging and non-imaging data (e.g. spectroscopy, proteomics). The major problem in such quantitative data integration lies in reconciling the large spread in the range of dimensionalities and scales across the different modalities. The primary goal of quantitative data integration is to build combined meta-classifiers; however these efforts are thwarted by challenges in (1) homogeneous representation of the data channels, (2) fusing the attributes to construct an integrated feature vector, and (3) the choice of learning strategy for training the integrated classifier. In this paper, we seek to (a) define the characteristics that guide the 4 independent methods for quantitative data fusion that use the idea of a meta-space for building integrated multi-modal, multi-scale meta-classifiers, and (b) attempt to understand the key components which allowed each method to succeed. These methods include (1) Generalized Embedding Concatenation (GEC), (2) Consensus Embedding (CE), (3) Semi-Supervised Multi-Kernel Graph Embedding (SeSMiK), and (4) Boosted Embedding Combination (BEC). In order to evaluate the optimal scheme for fusing imaging and non-imaging data, we compared these 4 schemes for the problems of combining (a) multi-parametric MRI with spectroscopy for prostate cancer (CaP) diagnosis in vivo, and (b) histological image with proteomic signatures (obtained via mass spectrometry) for predicting prognosis in CaP patients. The kernel combination approach (SeSMiK) marginally outperformed the embedding combination schemes. Additionally, intelligent weighting of the data channels (based on their relative importance) appeared to outperform unweighted strategies. All 4 strategies easily outperformed a nave decision fusion approach, suggesting that data integration methods will play an important role in the rapidly emerging field of integrated diagnostics and personalized healthcare. © 2011 IEEE.\n
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\n\n \n \n 2010\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n Computer-assisted targeted therapy (CATT) for prostate radiotherapy planning by fusion of CT and MRI.\n \n \n \n\n\n \n Chappelow, J.; Both, S.; Viswanath, S.; Hahn, S.; Feldman, M.; Rosen, M.; Tomaszewski, J.; Vapiwala, N.; Patel, P.; and Madabhushi, A.\n\n\n \n\n\n\n 2010.\n \n\n\n\n
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\n\n\n@misc{Chappelow2010,\n abstract = {In this paper, we present a comprehensive, quantitative imaging framework for improved treatment of prostate cancer via computer-assisted targeted therapy (CATT) to facilitate radiotherapy dose escalation to regions with a high likelihood of disease presence. The framework involves identification of high likelihood prostate cancer regions using computer-aided detection (CAD) classifier on diagnostic MRI, followed by mapping of these regions from MRI onto planning computerized tomography (CT) via image registration. Treatment of prostate cancer by targeted radiotherapy requires CT to formulate a dose plan. While accurate delineation of the prostate and cancer can provide reduced exposure of benign tissue to radiation, as well as a higher dose to the cancer, CT is ineffective in localizing intraprostatic lesions and poor for highlighting the prostate boundary. MR imagery on the other hand allows for greatly improved visualization of the prostate. Further, several studies have demonstrated the utility of CAD for identifying the location of tumors on in vivo multi-functional prostate MRI. Consequently, our objective is to improve the accuracy of radiotherapy dose plans via multimodal fusion of MR and CT. To achieve this objective, the CATT framework presented in this paper comprises the following components: (1) an unsupervised pixel-wise classifier to identify suspicious regions within the prostate on diagnostic MRI, (2) elastic image registration to align corresponding diagnostic MRI, planning MRI, and CT of the prostate, (3) mapping of the suspect regions from diagnostic MRI onto CT, and (4) calculation of a modified radiotherapy plan with escalated dose for cancer. Qualitative comparison of the dose plans (with and without CAD) over a total of 79 2D slices obtained from 10 MR-CT patient studies, suggest that our CATT framework could help in improved targeted treatment of prostate cancer.},\n author = {J.C. Chappelow and S. Both and S.E. Viswanath and S.M. Hahn and M.D. Feldman and M.A. Rosen and J.E. Tomaszewski and N. Vapiwala and P. Patel and A. Madabhushi},\n doi = {10.1117/12.844653},\n isbn = {9780819480262},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {CAD,Cancer,Clustering,Computerized tomography,IMRT,Image registration,Magnetic resonance imaging,Non-linear dimensionality reduction,Prostate,Targeted therapy,Texture,Therapy planning},\n title = {Computer-assisted targeted therapy (CATT) for prostate radiotherapy planning by fusion of CT and MRI},\n volume = {7625},\n year = {2010},\n}\n\n\n In this paper, we present a comprehensive, quantitative imaging framework for improved treatment of prostate cancer via computer-assisted targeted therapy (CATT) to facilitate radiotherapy dose escalation to regions with a high likelihood of disease presence. The framework involves identification of high likelihood prostate cancer regions using computer-aided detection (CAD) classifier on diagnostic MRI, followed by mapping of these regions from MRI onto planning computerized tomography (CT) via image registration. Treatment of prostate cancer by targeted radiotherapy requires CT to formulate a dose plan. While accurate delineation of the prostate and cancer can provide reduced exposure of benign tissue to radiation, as well as a higher dose to the cancer, CT is ineffective in localizing intraprostatic lesions and poor for highlighting the prostate boundary. MR imagery on the other hand allows for greatly improved visualization of the prostate. Further, several studies have demonstrated the utility of CAD for identifying the location of tumors on in vivo multi-functional prostate MRI. Consequently, our objective is to improve the accuracy of radiotherapy dose plans via multimodal fusion of MR and CT. To achieve this objective, the CATT framework presented in this paper comprises the following components: (1) an unsupervised pixel-wise classifier to identify suspicious regions within the prostate on diagnostic MRI, (2) elastic image registration to align corresponding diagnostic MRI, planning MRI, and CT of the prostate, (3) mapping of the suspect regions from diagnostic MRI onto CT, and (4) calculation of a modified radiotherapy plan with escalated dose for cancer. Qualitative comparison of the dose plans (with and without CAD) over a total of 79 2D slices obtained from 10 MR-CT patient studies, suggest that our CATT framework could help in improved targeted treatment of prostate cancer.\n
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\n\n \n \n 2009\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 tesla MRI.\n \n \n \n\n\n \n Viswanath, S.; Bloch, B.; Rosen, M.; Chappelow, J.; Toth, R.; Rofsky, N.; Lenkinski, R.; Genega, E.; Kalyanpur, A.; and Madabhushi, A.\n\n\n \n\n\n\n 2009.\n \n\n\n\n
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\n\n\n@misc{Viswanath2009,\n abstract = {Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.©2009 SPIE.},\n author = {S.E. Viswanath and B.N. Bloch and M.A. Rosen and J.C. Chappelow and R. Toth and N.M. Rofsky and R.E. Lenkinski and E.M. Genega and A. Kalyanpur and A. Madabhushi},\n doi = {10.1117/12.811899},\n isbn = {9780819475114},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {3 Tesla,Bagging,CAD,DCE-MRI,Data fusion,Decision fusion,Decision trees,Multimodal integration,Non-rigid registration,Prostate cancer,Random forests,Segmentation,Supervised learning,T2-w MRI},\n title = {Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 tesla MRI},\n volume = {7260},\n year = {2009},\n}\n\n\n Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.©2009 SPIE.\n
\n\n\n\n \n\n \n \n \n \n \n COLLINARUS: Collection of image-derived non-linear attributes for registration using splines.\n \n \n \n\n\n \n Chappelow, J.; Bloch, B.; Rofsky, N.; Genega, E.; Lenkinski, R.; DeWolf, W.; Viswanath, S.; and Madabhushi, A.\n\n\n \n\n\n\n 2009.\n \n\n\n\n
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\n\n\n@misc{Chappelow2009,\n abstract = {We present a new method for fully automatic non-rigid registration of multimodal imagery, including structural and functional data, that utilizes multiple texutral feature images to drive an automated spline based non-linear image registration procedure. Multimodal image registration is significantly more complicated than registration of images from the same modality or protocol on account of difficulty in quantifying similarity between different structural and functional information, and also due to possible physical deformations resulting from the data acquisition process. The COFEMI technique for feature ensemble selection and combination has been previously demonstrated to improve rigid registration performance over intensity-based MI for images of dissimilar modalities with visible intensity artifacts. Hence, we present here the natural extension of feature ensembles for driving automated non-rigid image registration in our new technique termed Collection of Image-derived Non-linear Attributes for Registration Using Splines (COLLINARUS). Qualitative and quantitative evaluation of the COLLINARUS scheme is performed on several sets of real multimodal prostate images and synthetic multiprotocol brain images. Multimodal (histology and MRI) prostate image registration is performed for 6 clinical data sets comprising a total of 21 groups of in vivo structural (T2-w) MRI, functional dynamic contrast enhanced (DCE) MRI, and ex vivo WMH images with cancer present. Our method determines a non-linear transformation to align WMH with the high resolution in vivo T2-w MRI, followed by mapping of the histopathologic cancer extent onto the T2-w MRI. The cancer extent is then mapped from T2-w MRI onto DCE-MRI using the combined non-rigid and affine transformations determined by the registration. Evaluation of prostate registration is performed by comparison with the 3 time point (3TP) representation of functional DCE data, which provides an independent estimate of cancer extent. The set of synthetic multiprotocol images, acquired from the BrainWeb Simulated Brain Database, comprises 11 pairs of T1-w and proton density (PD) MRI of the brain. Following the application of a known warping to misalign the images, non-rigid registration was then performed to recover the original, correct alignment of each image pair. Quantitative evaluation of brain registration was performed by direct comparison of (1) the recovered deformation field to the applied field and (2) the original undeformed and recovered PD MRI. For each of the data sets, COLLINARUS is compared with the MI-driven counterpart of the B-spline technique. In each of the quantitative experiments, registration accuracy was found to be significantly (p < 0.05) for COLLINARUS compared with MI-driven B-spline registration. Over 11 slices, the mean absolute error in the deformation field recovered by COLLINARUS was found to be 0.8830 mm. © 2009 Copyright SPIE - The International Society for Optical Engineering.},\n author = {J.C. Chappelow and B.N. Bloch and N.M. Rofsky and E.M. Genega and R.E. Lenkinski and W.C. DeWolf and S.E. Viswanath and A. Madabhushi},\n doi = {10.1117/12.812352},\n isbn = {9780819475107},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {B-splines,COFEMI,Cancer,Hierarchical,Image registration,prostate,Magnetic resonance,Non-rigid,Quantitative image analysis,Whole-mount histology},\n title = {COLLINARUS: Collection of image-derived non-linear attributes for registration using splines},\n volume = {7259},\n year = {2009},\n}\n\n\n We present a new method for fully automatic non-rigid registration of multimodal imagery, including structural and functional data, that utilizes multiple texutral feature images to drive an automated spline based non-linear image registration procedure. Multimodal image registration is significantly more complicated than registration of images from the same modality or protocol on account of difficulty in quantifying similarity between different structural and functional information, and also due to possible physical deformations resulting from the data acquisition process. The COFEMI technique for feature ensemble selection and combination has been previously demonstrated to improve rigid registration performance over intensity-based MI for images of dissimilar modalities with visible intensity artifacts. Hence, we present here the natural extension of feature ensembles for driving automated non-rigid image registration in our new technique termed Collection of Image-derived Non-linear Attributes for Registration Using Splines (COLLINARUS). Qualitative and quantitative evaluation of the COLLINARUS scheme is performed on several sets of real multimodal prostate images and synthetic multiprotocol brain images. Multimodal (histology and MRI) prostate image registration is performed for 6 clinical data sets comprising a total of 21 groups of in vivo structural (T2-w) MRI, functional dynamic contrast enhanced (DCE) MRI, and ex vivo WMH images with cancer present. Our method determines a non-linear transformation to align WMH with the high resolution in vivo T2-w MRI, followed by mapping of the histopathologic cancer extent onto the T2-w MRI. The cancer extent is then mapped from T2-w MRI onto DCE-MRI using the combined non-rigid and affine transformations determined by the registration. Evaluation of prostate registration is performed by comparison with the 3 time point (3TP) representation of functional DCE data, which provides an independent estimate of cancer extent. The set of synthetic multiprotocol images, acquired from the BrainWeb Simulated Brain Database, comprises 11 pairs of T1-w and proton density (PD) MRI of the brain. Following the application of a known warping to misalign the images, non-rigid registration was then performed to recover the original, correct alignment of each image pair. Quantitative evaluation of brain registration was performed by direct comparison of (1) the recovered deformation field to the applied field and (2) the original undeformed and recovered PD MRI. For each of the data sets, COLLINARUS is compared with the MI-driven counterpart of the B-spline technique. In each of the quantitative experiments, registration accuracy was found to be significantly (p < 0.05) for COLLINARUS compared with MI-driven B-spline registration. Over 11 slices, the mean absolute error in the deformation field recovered by COLLINARUS was found to be 0.8830 mm. © 2009 Copyright SPIE - The International Society for Optical Engineering.\n
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\n \n \n\n \n \n \n \n \n Improving supervised classification accuracy using non-rigid multimodal image registration: Detecting prostate cancer.\n \n \n \n\n\n \n Chappelow, J.; Viswanath, S.; Monaco, J.; Rosen, M.; Tomaszewski, J.; Feldman, M.; and Madabhushi, A.\n\n\n \n\n\n\n 2008.\n \n\n\n\n
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\n\n\n@misc{Chappelow2008,\n abstract = {Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP): the most common source for these labels is expert segmentations. When ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment. of the two modalities, in order to improve the quality of training data and hence classifier performance. We quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration methods were affine schemes; one based on maximization of mutual information (MT) and the other method that we previously developed, Combined Feature Ensemble Mutual Information (COFEMT), which incorporates high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by succeeding the two alline registration methods with an elastic deformation step using thin-plate splines (TPS). In the absence of definitive ground truth for CaP extent on MH1, classifier accuracy was evaluated against 7 ground truth surrogates obtained by different combinations of the expert and registration segmentations. For 26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver operating characteristic curve compared to that obtained from expert annotation. These results suggest that in the presence of additional multimodal image information one can obtain more accurate object annotations than achievable via expert delineation despite vast differences between modalities that hinder image registration.},\n author = {J.C. Chappelow and S.E. Viswanath and J.P. Monaco and M.A. Rosen and J.E. Tomaszewski and M.D. Feldman and A. Madabhushi},\n doi = {10.1117/12.770703},\n isbn = {9780819470997},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Analysis,Bayesian classifier,CAD,COFEMI,Dimensionality reduction,Histology,Independent component,MRI,Multimodal,Mutual information,Non-rigid,Prostate cancer,Registration,Thin plate splines},\n title = {Improving supervised classification accuracy using non-rigid multimodal image registration: Detecting prostate cancer},\n volume = {6915},\n year = {2008},\n}\n\n\n Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP): the most common source for these labels is expert segmentations. When ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment. of the two modalities, in order to improve the quality of training data and hence classifier performance. We quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration methods were affine schemes; one based on maximization of mutual information (MT) and the other method that we previously developed, Combined Feature Ensemble Mutual Information (COFEMT), which incorporates high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by succeeding the two alline registration methods with an elastic deformation step using thin-plate splines (TPS). In the absence of definitive ground truth for CaP extent on MH1, classifier accuracy was evaluated against 7 ground truth surrogates obtained by different combinations of the expert and registration segmentations. For 26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver operating characteristic curve compared to that obtained from expert annotation. These results suggest that in the presence of additional multimodal image information one can obtain more accurate object annotations than achievable via expert delineation despite vast differences between modalities that hinder image registration.\n
\n\n\n\n \n\n \n \n \n \n \n A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging.\n \n \n \n\n\n \n Viswanath, S.; Tiwari, P.; Rosen, M.; and Madabhushi, A.\n\n\n \n\n\n\n 2008.\n \n\n\n\n
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\n\n\n@misc{Viswanath2008,\n abstract = {Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE). Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities following image registration. However multimodal data integration is non-trivial when the individual modalities include spectral and image intensity data. We propose a data combination solution wherein we project the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional embedding space via NLDR. NLDR methods preserve the relationships between the objects in the original high dimensional space when projecting them into the reduced low dimensional space. Since the original spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional space, data at the same spatial location can be integrated by concatenating the respective embedding vectors. Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared to corresponding CAD results with the individual modalities.},\n author = {S.E. Viswanath and P. Tiwari and M.A. Rosen and A. Madabhushi},\n doi = {10.1117/12.771022},\n isbn = {9780819470997},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {Automated detection,Combination schemes,Computer aided diagnosis,Magnetic Resonance Imaging (MRI),Magnetic Resonance Spectroscopy (MRS),Multimodal integration,Prostate cancer},\n title = {A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging},\n volume = {6915},\n year = {2008},\n}\n\n\n Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE). Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities following image registration. However multimodal data integration is non-trivial when the individual modalities include spectral and image intensity data. We propose a data combination solution wherein we project the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional embedding space via NLDR. NLDR methods preserve the relationships between the objects in the original high dimensional space when projecting them into the reduced low dimensional space. Since the original spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional space, data at the same spatial location can be integrated by concatenating the respective embedding vectors. Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared to corresponding CAD results with the individual modalities.\n
\n\n\n\n \n\n \n \n \n \n \n A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery.\n \n \n \n\n\n \n Viswanath, S.; Rosen, M.; and Madabhushi, A.\n\n\n \n\n\n\n 2008.\n \n\n\n\n
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\n\n\n@misc{Viswanath2008,\n abstract = {Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multisite ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.},\n author = {S.E. Viswanath and M.A. Rosen and A. Madabhushi},\n doi = {10.1117/12.770868},\n isbn = {9780819470997},\n issn = {16057422},\n journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},\n keywords = {1.5 Tesla,Computer-aided diagnosis,Consensus clustering,Consensus embedding,In vivo,MRI,Manifold learning,Prostate cancer,Segmentation},\n title = {A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery},\n volume = {6915},\n year = {2008},\n}\n\n\n Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multisite ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.\n
\n\n\n\n \n\n \n \n \n \n \n A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI.\n \n \n \n\n\n \n Viswanath, S.; Bloch, B.; Genega, E.; Rofsky, N.; Lenkinski, R.; Chappelow, J.; Toth, R.; and Madabhushi, A.\n\n\n \n\n\n\n 2008.\n \n\n\n\n
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\n\n\n@misc{Viswanath2008,\n abstract = {Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising modality for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function of contrast agent uptake by the lesion. Comparatively there is relatively little work in developing CAD schemes for prostate DCE-MRI. In this paper, we present a novel unsupervised detection scheme for CaP from 3 T DCE-MRI which comprises 3 distinct steps. First, a multi-attribute active shape model is used to automatically segment the prostate boundary from 3 T in vivo MR imagery. A robust multimodal registration scheme is then used to non-linearly align corresponding whole mount histological and DCE-MRI sections from prostatectomy specimens to determine the spatial extent of CaP. Non-linear dimensionality reduction schemes such as locally linear embedding (LLE) have been previously shown to be useful in projecting such high dimensional biomedical data into a lower dimensional subspace while preserving the non-linear geometry of the data manifold. DCE-MRI data is embedded via LLE and then classified via unsupervised consensus clustering to identify distinct classes. Quantitative evaluation on 21 histology-MRI slice pairs against registered CaP ground truth estimates yielded a maximum CaP detection accuracy of 77.20% while the popular three time point (3TP) scheme yielded an accuracy of 67.37%. © 2008 Springer-Verlag Berlin Heidelberg.},\n author = {S.E. Viswanath and B.N. Bloch and E.M. Genega and N.M. Rofsky and R.E. Lenkinski and J.C. Chappelow and R. Toth and A. Madabhushi},\n doi = {10.1007/978-3-540-85988-8_79},\n isbn = {354085987X},\n issn = {03029743},\n issue = {PART 1},\n journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n title = {A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI},\n volume = {5241 LNCS},\n year = {2008},\n}\n\n\n Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising modality for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function of contrast agent uptake by the lesion. Comparatively there is relatively little work in developing CAD schemes for prostate DCE-MRI. In this paper, we present a novel unsupervised detection scheme for CaP from 3 T DCE-MRI which comprises 3 distinct steps. First, a multi-attribute active shape model is used to automatically segment the prostate boundary from 3 T in vivo MR imagery. A robust multimodal registration scheme is then used to non-linearly align corresponding whole mount histological and DCE-MRI sections from prostatectomy specimens to determine the spatial extent of CaP. Non-linear dimensionality reduction schemes such as locally linear embedding (LLE) have been previously shown to be useful in projecting such high dimensional biomedical data into a lower dimensional subspace while preserving the non-linear geometry of the data manifold. DCE-MRI data is embedded via LLE and then classified via unsupervised consensus clustering to identify distinct classes. Quantitative evaluation on 21 histology-MRI slice pairs against registered CaP ground truth estimates yielded a maximum CaP detection accuracy of 77.20% while the popular three time point (3TP) scheme yielded an accuracy of 67.37%. © 2008 Springer-Verlag Berlin Heidelberg.\n
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