NIMG-75. SIMILARITY DISTANCES-BASED DELTA RADIOMICS (SDDR) ENABLES LONGITUDINAL MRI COMPARISONS FOR SURVIVAL PREDICTION IN GLIOMAS: A MULTI-INSTITUTIONAL VALIDATION

Abstract Radiomics (computerized feature analysis) on treatment-naive MRI scans has demonstrated great value in outcome prediction for Glioblastoma (GBM). However, delta radiomics (analysis of radiomic feature variation between different events, e.g., before and after treatment) has not been explored on account of challenges with precise spatial correspondences between pre- and post-operative scans. This work presents a survival prediction model for GBM, Similarity Distances-based delta radiomics (SDDR), based on the hypothesis that similarity distance-measures between radiomic features on pre-, and post-treatment images can accurately capture temporal changes within-and-around the tumor, and provide improved prediction of overall survival, compared to models based on a single timepoint. 150 patients from 2 publicly-available Glioma repositories (University of California San Francisco “UCSF” (n=48), LUMIERE (n=67)) and 2 two proprietary datasets (from xCures (n=13), and Cleveland Clinic (CCF) (n=74)) were obtained. UCSF and LUMIERE cohorts were used as separate hold-out sets, in each of which the remaining collections were used as discovery set. Following pre-processing, tumor segmentation into Edema (ED) and Enhancing tumor (ET) were performed. SDDR was then computed, which is the distance between probability densities functions from 156 gray co-occurrence (GLCM) and 52 gradient-based co-occurrences statistics (COLLAGE) features extracted from pre and-post-treatment MRI. Six statistical distances (e.g., Earth mover distance) normalized by the scanning interval (in weeks) yielded n=2496 SDDR measurements per tumor region (1248/region, 936 delta-GLCM, and 312 delta-COLLAGE). The same set of features were computed from pretreatment MRI (Pre-R) for comparison. Features were fed to LASSO-Cox regression for survival analysis. SDDR for GLCM features extracted from ET outperformed Pre-R, and demonstrated significant differences between high and low risk groups for both hold out tests: UCSF (p=0.0069, HR =2.8, C-index=0.69) and LUMIERE (p=0.0061, HR = 1.9, C-index 0.65). SDDR-based multi-institutional survival analysis demonstrated promise in reliable longitudinal risk-stratification in GBM.