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The study aims to develop and validate a model for predicting recurrent gliomas using MRI and machine learning techniques.

June 27, 2026Open Access

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

Key Points

The study aims to develop and validate a model for predicting recurrent gliomas using MRI and machine learning techniques.
Developed machine learning models with 254 MRI patches from recurrent surgery tissue samples.
Robust evaluation and clinical validation performed using a test set with AUROC analysis.
Associated volumetrics from prediction maps with survival data in a separate cohort of 56 patients.
Achieved a test AUROC of 0.74 ± 0.08 for recurrent tumors and 0.99 ± 0.01 for normal brain.
Important predictive features aligned with existing literature, showing a correlation with model uncertainty (p ≤ 0.05).
Volumetrics from prediction maps significantly associated with patient survival.

Abstract

Abstract 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.

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