Glioblastoma recurrence remains nearly inevitable despite maximal resection and adjuvant therapy, with most relapses occurring within or adjacent to the original tumor site. Conventional MRI underestimates tumor infiltration beyond contrast-enhancing margins, limiting preoperative identification of peritumoral regions at higher risk of recurrence. We developed a radiomics-based machine-learning framework to generate preoperative spatial recurrence risk maps from routine MRI. Preoperative T1-weighted contrast-enhanced and FLAIR images from 79 patients with glioblastoma were analyzed. The cohort was divided at the patient level into a training set (n = 63) and an independent test set (n = 16). Using a balanced spatial ROI sampling strategy, local radiomic features were extracted from tumor and peritumoral regions and linked to recurrence sites identified on follow-up MRI acquired after at least 12 months after surgery. A CatBoost classifier achieved an AUC of 0.743 and a recall of 0.856 on an independent test set. The framework also generated preoperative probability maps that showed qualitative spatial correspondence with observed recurrence locations. These findings indicate that radiomic patterns from standard preoperative MRI may contain spatially localized information associated with future relapse. The proposed approach supports the feasibility of preoperative spatial risk stratification and may provide useful information for surgical planning and subsequent treatment strategies.
Seoni et al. (2026) studied this question.