arXiv 2310.014132023-10-02eess.IVcs.AIcs.CV

儿童脑肿瘤网络（Children's Brain Tumor Network）构建的多机构儿科临床放射学MRI数据集

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network

Children’s Hospital of Philadelphia（费城儿童医院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；National Cancer Institute（美国国家癌症研究所）；Children's National Hospital（儿童国家医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎耶德儿科外科创新研究院）

Ariana M. Familiar, Anahita Fathi Kazerooni, Hannah Anderson, Aliaksandr Lubneuski, Karthik Viswanathan, Rocky Breslow, Nastaran Khalili, Sina Bagheri, Debanjan… 展开作者

Ariana M. Familiar, Anahita Fathi Kazerooni, Hannah Anderson, Aliaksandr Lubneuski, Karthik Viswanathan, Rocky Breslow, Nastaran Khalili, Sina Bagheri, Debanjan Haldar, Meen Chul Kim, Sherjeel Arif, Rachel Madhogarhia, Thinh Q. Nguyen, Elizabeth A. Frenkel, Zeinab Helili, Jessica Harrison, Keyvan Farahani, Marius George Linguraru, Ulas Bagci, Yury Velichko, Jeffrey Stevens, Sarah Leary, Robert M. Lober, Stephani Campion, Amy A. Smith, Denise Morinigo, Brian Rood, Kimberly Diamond, Ian F. Pollack, Melissa Williams, Arastoo Vossough, Jeffrey B. Ware, Sabine Mueller, Phillip B. Storm, Allison P. Heath, Angela J. Waanders, Jena V. Lilly, Jennifer L. Mason, Adam C. Resnick, Ali Nabavizadeh

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AI总结：

针对儿科神经肿瘤影像数据不足的问题，儿童脑肿瘤网络发布了含1526名患者23101次多参数MRI及多维度配套数据的多机构大规模数据集，以加速AI研究和儿童精准医疗发展。

摘要：

儿童脑癌和脊髓癌仍是儿童癌症相关死亡的首要原因。然而，利用常规诊疗中收集的大量放射影像数据推进儿科神经肿瘤学临床决策支持的进展，已显著落后于其他领域。这类数据非常适合用于人工智能（AI）方法等预测分析，而这类方法需要大规模数据集。为满足这一未被满足的需求，我们作为儿童脑肿瘤网络（Children's Brain Tumor Network）的一部分，提供了一个多机构的大规模儿科数据集，包含为1526名脑肿瘤患者通过常规诊疗采集的23101次多参数MRI检查。该数据集涵盖多种癌症诊断的纵向MRI影像，以及配套的患者层面临床信息、数字病理切片，还有组织基因型和组学数据。为便于下游分析，370名受试者的未接受治疗影像已通过癌症数据服务（Cancer Data Service）在NCI儿童癌症数据倡议（NCI Childhood Cancer Data Initiative）中处理并发布。通过持续建设这些影像库，我们旨在借助真实世界数据加速研究发现和可转化AI模型的开发，最终助力实现儿童精准医疗。

英文摘要：

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。