构建开放平台提升胶质母细胞瘤放疗精准度，预测复发区域。
PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning
- 整合243例多中心患者数据与标准化评估流程，支持模型开发与验证。
- U-Net模型预测复发覆盖率达79.37%，显著优于传统放疗方案（p=5.7e-6）。
- 平台开源，助力个性化放疗研究，适合医学影像与计算模型团队使用。
胶质母细胞瘤复发主要由影像学可见边界外的弥散浸润驱动，但标准放疗依赖统一扩增，忽视个体生物学与解剖特征。尽管计算模型可预测隐匿性生长并指导个性化治疗，其临床转化受限于缺乏标准化、大规模基准测试与可复现验证流程。为此，我们提出PREDICT-GBM——一个整合243例患者纵向多中心数据集与标准化评估流水线的开源平台，支持模型开发与验证。通过训练并对比新型U-Net复发预测模型与先进生物物理及数据驱动方法，结果表明：生物物理与深度学习方法均显著优于标准护理方案，在保持等体积治疗约束下预测未来复发位置。其中U-Net模型实现79.37% ± 2.08%的增强复发覆盖（配对Wilcoxon检验，p=0.0000057），GliODIL生物物理模型达78.91% ± 2.08%（p=0.00045）。该平台首次建立严谨、可复现的模型训练与验证生态系统，消除个性化计算引导放疗的主要障碍。本工作确立了计算引导个性化放疗新标准，平台、模型与数据已公开于github.com/BrainLesion/PredictGBM。
原文摘要 · Abstract (English)
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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