@book{
 title = {RADIomic spatial textural descriptor (RADISTAT): Characterizing intra-tumoral heterogeneity for response and outcome prediction},
 type = {book},
 year = {2017},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 volume = {10434 LNCS},
 id = {cf57e395-8d7b-3552-bf63-7dac6deefe66},
 created = {2023-10-25T08:56:40.097Z},
 file_attached = {false},
 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:40.097Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 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.},
 bibtype = {book},
 author = {Antunes, J. and Prasanna, P. and Madabhushi, A. and Tiwari, P. and Viswanath, S.},
 doi = {10.1007/978-3-319-66185-8_53}
}

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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.","bibtype":"book","author":"Antunes, J. and Prasanna, P. and Madabhushi, A. and Tiwari, P. and Viswanath, S.","doi":"10.1007/978-3-319-66185-8_53","bibtex":"@book{\n title = {RADIomic spatial textural descriptor (RADISTAT): Characterizing intra-tumoral heterogeneity for response and outcome prediction},\n type = {book},\n year = {2017},\n source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n volume = {10434 LNCS},\n id = {cf57e395-8d7b-3552-bf63-7dac6deefe66},\n created = {2023-10-25T08:56:40.097Z},\n file_attached = {false},\n profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},\n last_modified = {2023-10-25T08:56:40.097Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\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. 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