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

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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