Title: PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma Show Chinese title

Title: PREDICT-GBM：胶质母细胞瘤个体化计算肿瘤模型的稳健评估和开发平台

Abstract: Glioblastoma is the most prevalent primary brain malignancy, distinguished by its highly invasive behavior and exceptionally high rates of recurrence. Conventional radiation therapy, which employs uniform treatment margins, fails to account for patient-specific anatomical and biological factors that critically influence tumor cell migration. To address this limitation, numerous computational models of glioblastoma growth have been developed, enabling generation of tumor cell distribution maps extending beyond radiographically visible regions and thus informing more precise treatment strategies. However, despite encouraging preliminary findings, the clinical adoption of these growth models remains limited. To bridge this translational gap and accelerate both model development and clinical validation, we introduce PREDICT-GBM, a comprehensive integrated pipeline and dataset for modeling and evaluation. This platform enables systematic benchmarking of state-of-the-art tumor growth models using an expert-curated clinical dataset comprising 255 subjects with complete tumor segmentations and tissue characterization maps. Our analysis demonstrates that personalized radiation treatment plans derived from tumor growth predictions achieved superior recurrence coverage compared to conventional uniform margin approaches for two of the evaluated models. This work establishes a robust platform for advancing and systematically evaluating cutting-edge tumor growth modeling approaches, with the ultimate goal of facilitating clinical translation and improving patient outcomes. Show Chinese abstract

Abstract: 胶质母细胞瘤是最常见的原发性脑部恶性肿瘤，其特征是高度侵袭性行为和极高的复发率。 传统放疗疗法采用统一的治疗边缘，未能考虑对肿瘤细胞迁移起关键作用的患者特异性解剖和生物学因素。 为解决这一局限性，已经开发了多种胶质母细胞瘤生长的计算模型，能够生成超出影像学可见区域的肿瘤细胞分布图，从而制定更精确的治疗策略。 然而，尽管初步结果令人鼓舞，这些生长模型的临床应用仍然有限。 为了弥合这种转化差距并加速模型开发和临床验证，我们引入了PREDICT-GBM，这是一个全面的集成流程和数据集，用于建模和评估。 该平台利用专家精心整理的临床数据集，包括255名具有完整肿瘤分割和组织特征图的受试者，实现了对最先进的肿瘤生长模型的系统基准测试。 我们的分析表明，从肿瘤生长预测中得出的个性化放疗计划，在两种评估模型中相比传统的统一边缘方法，实现了更好的复发覆盖。 这项工作建立了一个强大的平台，以推进和系统评估前沿的肿瘤生长建模方法，最终目标是促进临床转化并改善患者预后。