Interpretable Survival Risk Prediction for High-Grade Glioma Patients via Radiomic Features from Peritumoral Region

Peritumoral edema regions carry prognostic value in patients with high-grade glioma (HGG), the most invasive type of brain cancer. Recent findings have established the association of texture and shape features extracted from these regions with survival outcomes. However, no study has converged on a single feature that significantly correlates with survival outcomes. In this study, we develop an automated and interpretable brain tumor patient survival risk prediction model using radiomic features from the peritumoral region of HGG. First, the peritumoral edema regions are segmented from MRI scans imaged using multiple modalities (T1, T2, FLAIR, T1-contrast enhanced) and compiled into the BraTS-2020 dataset. Texture and shape features extracted from the segmented regions were analyzed to stratify patients based on a risk score. The proposed framework demonstrates the significance of a texture and shape feature to predict survival outcomes for a subset of 76 HGG patients with survival information. Moreover, we conduct univariate and multivariable analysis to further demonstrate the clinical utility of the extracted texture and shape features. The study provides evidence for the importance of texture and shape features extracted from peritumoral edema regions in predicting survival outcomes in HGG patients. It may facilitate personalized treatment and improve the prognostic accuracy of HGG patients in real-world clinical setting.