Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI. Shiradkar, R., Podder, T., Algohary, A., Viswanath, S., Ellis, R., & Madabhushi, A. Radiation Oncology, 2016.
doi  abstract   bibtex   
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.
@article{
 title = {Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI},
 type = {article},
 year = {2016},
 keywords = {Computer aided diagnosis (CAD),Prostate cancer,Radiomics,Treatment planning},
 volume = {11},
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 created = {2023-10-25T08:56:40.142Z},
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 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:40.142Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 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 (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.},
 bibtype = {article},
 author = {Shiradkar, R. and Podder, T.K. and Algohary, A. and Viswanath, S. and Ellis, R.J. and Madabhushi, A.},
 doi = {10.1186/s13014-016-0718-3},
 journal = {Radiation Oncology},
 number = {1}
}
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