Spherical Radiomics for Radiogenomic Assessment of Glioblastoma Heterogeneity

Abstract Background To develop and validate a novel spherical radiomics framework for predicting key molecular biomarkers—including MGMT promoter methylation, EGFR, and PTEN mutation status—and survival in glioblastoma (GBM) patients using multiparametric MRI. Methods Using the UCSF-PDGM cohort, we propose a spherical radiomics framework in which tumor-centered concentric shells are generated at increasing radial distances from the tumor centroid and mapped onto two-dimensional surfaces for feature extraction. Radiomic features—including shape, first-order statistics, and texture descriptors (GLCM, GLRLM, GLDM, GLSZM, NGTDM)—were extracted from 299 GBM patients using PyRadiomics across four tumor regions: necrotic core, T1-weighted contrast-enhancing region, T2/FLAIR hyperintense lesion, and a 2 cm peritumoral expansion region. Classification was performed using multiple machine learning models, including neural networks, logistic regression, random forest, and the Tree-based Pipeline Optimization Tool (TPOT). The proposed framework was further validated on an external UPENN-GBM cohort. Model interpretability was assessed using SHAP analysis, feature significance profiling, clustering visualization, and evaluation of radiomic patterns in relation to underlying biological processes. Radial transition analysis was conducted to quantify feature changes across adjacent tumor regions. Results Spherical radiomics achieved AUCs of 0.82 for MGMT, 0.77 for EGFR, 0.76 for PTEN, and 0.79 for survival prediction, consistently outperforming conventional Euclidean radiomics (0.71, 0.61, 0.70, and 0.61, respectively). Consistent improvements were also observed in external validation based on UPENN-GBM cohort, with an AUC improved by 8% compared to Euclidean radiomics. GLCM-derived features were identified as the most informative predictors. Radial transition analysis using the Mann–Whitney U-test demonstrated that transition slopes between the T1-weighted contrast-enhancing and T2/FLAIR hyperintense regions, as well as between the T2/FLAIR hyperintense and peritumoral regions, were significantly associated with biomarker status. Conclusion Radiomic features extracted from spherical surfaces at varying radial distances from the tumor centroid demonstrate stronger associations with key molecular markers and patient survival compared to conventional Euclidean radiomics, highlighting the value of spatially structured radiomic analysis for improved GBM characterization.