Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures

IF 48.8 1区 医学 Q1 CELL BIOLOGY
Yan-Ran (Joyce) Wang, Pengcheng Wang, Zihan Yan, Quan Zhou, Fatma Gunturkun, Peng Li, Yanshen Hu, Wei Emma Wu, Kankan Zhao, Michael Zhang, Haoyi Lv, Lehao Fu, Jiajie Jin, Qing Du, Haoyu Wang, Kun Chen, Liangqiong Qu, Keldon Lin, Michael Iv, Hao Wang, Jian Gong
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Abstract

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, comparisons between East Asia and North America subsets highlight critical management implications. We made this comprehensive dataset, which includes MRI signatures, clinicopathological features, treatment variables, and survival data, publicly available to advance global medulloblastoma research.

Abstract Image

利用人工智能和磁共振成像特征推进髓母细胞瘤术前非侵入性分子亚组预测
髓母细胞瘤的全球研究一直受阻于分子亚组检测的普及和数据的匮乏。为了弥补这一缺陷,我们建立了一个国际分子特征数据库,其中包括来自中国和美国十三个中心的 934 名髓母细胞瘤患者。我们展示了基于图像的机器学习策略如何在髓母细胞瘤的临床治疗中为无创、术前和低成本的分子亚组预测开辟另一条途径。我们强有力的验证策略--包括交叉验证、外部验证和连续验证--证明了该模型作为可推广的分子诊断分类器的有效性。对核磁共振成像特征的详细分析通过细微的放射学视角补充了对髓母细胞瘤的认识。此外,东亚和北美子集之间的比较凸显了重要的管理意义。我们公开了这一综合数据集,其中包括磁共振成像特征、临床病理特征、治疗变量和生存数据,以推动全球髓母细胞瘤研究。
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来源期刊
Cancer Cell
Cancer Cell 医学-肿瘤学
CiteScore
55.20
自引率
1.20%
发文量
179
审稿时长
4-8 weeks
期刊介绍: Cancer Cell is a journal that focuses on promoting major advances in cancer research and oncology. The primary criteria for considering manuscripts are as follows: Major advances: Manuscripts should provide significant advancements in answering important questions related to naturally occurring cancers. Translational research: The journal welcomes translational research, which involves the application of basic scientific findings to human health and clinical practice. Clinical investigations: Cancer Cell is interested in publishing clinical investigations that contribute to establishing new paradigms in the treatment, diagnosis, or prevention of cancers. Insights into cancer biology: The journal values clinical investigations that provide important insights into cancer biology beyond what has been revealed by preclinical studies. Mechanism-based proof-of-principle studies: Cancer Cell encourages the publication of mechanism-based proof-of-principle clinical studies, which demonstrate the feasibility of a specific therapeutic approach or diagnostic test.
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