RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment
Year: 2022
Type: article
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.
Institution: Case Western Reserve University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: U.S. Department of Veterans Affairs, Wallace H. Coulter Foundation, Case Western Reserve University, National Institutes of Health, Johnson and Johnson, Clinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve University, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, National Center for Advancing Translational Sciences, DOD Prostate Cancer Research Program, DOD Peer Reviewed Cancer Research Program
Awards: RadxTools for assessing tumor treatment response on imaging, G1505200503, G1508390373, G1828163000, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, Pathology Image Informatics Platform for visualization, analysis and management, A Study to Validate and Improve an Automated Image Analysis Algorithm to Detect Tuberculosis in Sputum Smear Slides, G2276198327, BIOMEDICAL ENGINEERING RESEARCH FACILITIES, G3054115203, G3068466812, G3541864657, G3766848304, Rad-path-omic tools for rectal cancer treatment evaluation, CHIRP Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung Cancers, G406353596, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, MR Fingerprinting and Computerized Decision Support for Prostate Cancer, Computerized histologic image predictor of cancer outcome, Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal Carcinoma, Computer-Assisted Histologic Evaluation of Cardiac Allograft Rejection, G761790451, G7935030457, G854738250, G86714149, Clinical and Translational Science Collaborative of Cleveland, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC), G9429638
OpenAlex ID: W4210427607