PREDICT-GBM：一个多中心平台以推进个性化胶质瘤放疗规划
PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning
AI总结：
本文提出PREDICT-GBM平台，整合多中心数据集和标准化评估流程，开发并验证了基于U-Net的复发预测模型，显著提升了放疗规划的个性化水平。
摘要：
胶质瘤复发主要由放射学可见肿瘤边缘外的弥散浸润驱动，但标准放疗依赖均匀扩展，忽视患者特异性生物和解剖因素。尽管计算模型有望映射这种不可见生长并指导个性化治疗规划，但临床应用受限于缺乏标准化、大规模基准测试和可重复验证的工作流程。为此，我们提出了PREDICT-GBM，一个综合的开源平台，整合了243名患者的精心挑选、纵向、多中心数据集，并提供标准化评估流程，推动模型开发和验证。我们通过训练和评估一种新的基于U-Net的复发预测模型，与最先进的生物物理和数据驱动方法进行比较，证明了PREDICT-GBM的潜力。我们的结果表明，生物物理和深度学习方法在预测未来复发部位方面显著优于标准治疗方案，同时维持等体积治疗约束。值得注意的是，我们的U-Net模型在增强复发覆盖方面达到了79.37±2.08%，明显优于标准治疗方案（配对Wilcoxon符号秩检验，p=0.0000057）。此外，生物物理模型GliODIL达到了78.91±2.08%（p=0.00045），验证了平台比较不同建模范式的潜力。通过提供首个严谨、可重复的模型训练和验证生态系统，PREDICT-GBM消除了个性化、计算引导放疗的主要瓶颈。本工作为开发计算引导的个性化放疗树立了新标准，平台、模型和数据已开源在github.com/BrainLesion/PredictGBM。
英文摘要：
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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