Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning

作者：Jacob Ellison, Nate Tran, Tracy Luks, Paramjot Singh, Angela Jakary, Tiffany Ngan, Julia Cluceru, Joanna Phillips, Y Li, Annette M Molinaro, Valentina Pedoia, Anny Shai, Devika Nair, Javier Villanueva-Meyer, Mitchel S. Berger, Shawn L. Hervey-Jumper, M. K. Aghi, Susan M. Chang, Janine Lupo · 发表于：npj Digital Medicine · 年份：2026 · DOI：10.1038/s41746-026-02823-y · 研究领域：Glioma Diagnosis and Treatment、Brain Tumor Detection and Classification、Radiomics and Machine Learning in Medical Imaging

Despite prior success in classifying recurrent glioma noninvasively with multi-parametric MRI and AI, clinical applicability has yet to be demonstrated due to a lack of robust model evaluation and spatial preservation of tumor characteristics. This study develops, robustly evaluates, and clinically validates an interpretable model for predicting recurrent tumors from spatially varying, histopathologically-confirmed tissue samples. Machine learning models were developed using 254 pre-surgical multi-parametric MRI patches surrounding coordinates of tissue samples taken during recurrent surgery. A test AUROC of 0.74 ± 0.08 for distinguishing recurrent tumors, and 0.99 ± 0.01 for normal-appearing brain, demonstrated the feasibility of spatially mapping heterogeneity. Important features were consistent with current literature, and uncertainty was correlated with model failures (p ≤ 0.05). Volumetrics derived from prediction maps of recurrent tumors generated using a separate cohort of 56 patients with recurrent high-grade gliomas were significantly associated with survival. These results demonstrate a step towards clinical applicability of spatially mapping glioma recurrence.