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arXiv · 2509.13360

PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning

L. Zimmer·J. Weidner·M. Balcerak·F. Kofler·M. Krupa·I. Ezhov·S. Cepeda·R. Zhang·J. Lowengrub·B. Menze·B. Wiestler
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Abstract

Glioblastoma recurrence is largely driven by diffuse infiltration beyond radiologically visible tumor margins, yet standard radiotherapy, the mainstay of glioblastoma treatment, relies on uniform expansions that ignore patient-specific biological and anatomical factors. While computational models promise to map this invisible growth and guide personalized treatment planning, their clinical translation is hindered by the lack of standardized, large-scale benchmarking and reproducible validation workflows. To bridge this gap, we present PREDICT-GBM, a comprehensive open-source platform that integrates a curated, longitudinal, multi-center dataset of 243 patients with a standardized evaluation pipeline, and fuels model development and validation. We demonstrate PREDICT-GBM's potential by training and benchmarking a novel U-Net-based recurrence prediction model against state-of-the-art biophysical and data-driven methods. Our results show that both biophysical and deep-learning approaches significantly outperform standard-of-care protocols in predicting future recurrence sites while maintaining iso-volumetric treatment constraints. Notably, our U-Net model achieved a superior coverage of enhancing recurrence (79.37 +/- 2.08 %), markedly surpassing the standard-of-care (paired Wilcoxon signed-rank test, p = 0.0000057). Furthermore, the biophysical model GliODIL reached 78.91 +/- 2.08 % (p = 0.00045), validating the platform's ability to compare diverse modeling paradigms. By providing the first rigorous, reproducible ecosystem for model training and validation, PREDICT-GBM eliminates a major bottleneck for personalized, computationally guided radiotherapy. This work establishes a new standard for developing computationally guided, personalized radiotherapy, with the platform, models, and data openly available at github.com/BrainLesion/PredictGBM

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L. Zimmer, J. Weidner, M. Balcerak, F. Kofler, M. Krupa, I. Ezhov, S. Cepeda, R. Zhang, J. Lowengrub, B. Menze, B. Wiestler. 2026-03-15. PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning. https://arxiv.org/abs/2509.13360

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