BIOM-100. MRI RADIOMIC FEATURE DYNAMICS DURING RADIOTHERAPY TO PREDICT RECURRENCE IN HIGH-GRADE GLIOMA

Abstract High-grade glioma (HGG) exhibits aggressive behavior and high recurrence rates, with limited reliable non-invasive biomarkers to predict outcomes. Radiomic analysis of longitudinal MRI may offer insights into tumor microenvironment changes associated with recurrence. We retrospectively analyzed 46 HGG patients enrolled in an ongoing clinical trial who underwent T1-weighted post-contrast MRI at baseline and midway through radiotherapy. Radiomic features (N=504) were extracted from 4 tumor subregions: enhancing lesion, edema, necrosis, and resection cavity using IBSI-compliant preprocessing and PyRadiomics. We evaluated changes in radiomic features between time points to distinguish non-recurring (n=9) from recurring (n=37) patients. Statistical significance was assessed via Mann-Whitney U tests with false discovery rate (FDR) correction. Discrimination and effect size were quantified by area under the ROC curve (AUC) and Cliff’s delta respectively. Changes in texture features from peritumoral edema showed the strongest association with recurrence. ‘Low Gray Level Emphasis’ demonstrated excellent discrimination (AUC=0.93, adjusted p=0.04, Cliff’s delta=-0.86), suggesting that increased homogeneity of low-intensity regions may represent hypoxic or immune-suppressed zones. Two additional features from regions of edema that indicate spatial clustering of hypointense voxels consistent with infiltrative spread or vasogenic edema: ‘Long Run Low Gray Level Emphasis’ (AUC = 0.88) and ‘Low Gray Level Run Emphasis’ (AUC = 0.87), showed strong uncorrected significance (p < 0.001). The ‘Run Variance’ from the enhancing lesion (AUC = 0.86, p = 0.0008) also correlated with recurrence but wasn’t significant after FDR correction. Radiomic changes in peritumoral edema from mid-treatment MRI may serve as promising biomarkers of HGG recurrence. The ‘Low Gray Level Emphasis’ feature in edema was the most robust, while other high-AUC texture features warrant further validation. These preliminary findings support the potential use of pre- and mid-treatment MRI radiomics in early risk stratification and personalized treatment planning.