Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we established an international molecularly characterized database encompassing 934 medulloblastoma patients from thirteen centers across China and the United States. We demonstrate how image-based machine learning strategies have the potential to create an alternative pathway for non-invasive, presurgical, and low-cost molecular subgroup prediction in the clinical management of medulloblastoma. Our robust validation strategies—including cross-validation, external validation, and consecutive validation—demonstrate the model’s efficacy as a generalizable molecular diagnosis classifier. The detailed analysis of MRI characteristics replenishes the understanding of medulloblastoma through a nuanced radiographic lens. Additionally …
本文构建了涵盖中国和美国13个中心934例髓母细胞瘤患者的国际分子特征数据库，利用人工智能和MRI影像特征实现术前无创的分子亚型预测。通过交叉验证、外部验证和连续验证，证明了模型作为通用分子诊断分类器的有效性，并通过对MRI特征的详细分析，从影像学角度深化了对髓母细胞瘤的理解，为临床管理提供了低成本、可推广的替代路径。