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
{"title":"Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures","authors":"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","doi":"10.1016/j.ccell.2024.06.002","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":9670,"journal":{"name":"Cancer Cell","volume":null,"pages":null},"PeriodicalIF":48.8000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Cell","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.ccell.2024.06.002","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
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.