Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma

Glioblastoma is characterized by diffuse infiltration, making accurate detection of residual disease essential for improving prognostication and guiding treatment. This study evaluates whether the volume of predicted infiltration, generated by a machine learning (ML) model trained on radiomic features from postoperative magnetic resonance imaging (MRI), is an independent prognostic factor. We analyzed a total of 114 glioblastoma patients, 89 from a retrospective multicenter cohort and 25 from a prospective cohort, who underwent gross total resection and had an early postoperative MRI. A previously published voxel-wise ML model estimated tumor infiltration probability in the non-enhancing peritumoral region using conventional MRI sequences. High-risk of recurrence regions (HRoR) were delineated from the probability maps, and their volumes were quantified. Associations with residual FLAIR volume, clinical variables (age, Karnofsky Performance Status), and survival outcomes (overall survival [OS], progression-free survival [PFS]) were evaluated using Cox regression and Kaplan–Meier analysis. In the retrospective cohort, multivariate Cox modeling confirmed that higher HRoR volume was independently associated with shorter OS (HR = 1.51; 95% CI, 1.12–2.05; p = 0.008), with no association found for PFS. A robust cutoff of 1.6 cm³ stratified patients into high- and low-risk groups with significantly different OS (456 vs. 678 days; p = 0.038). This threshold was validated in a prospective cohort (326 vs. 525 days; p = 0.039). ML-derived HRoR mapping provides independent prognostic value and may improve risk stratification after surgery in glioblastoma. These findings support its potential clinical integration for personalized follow-up and treatment.