Multi-Site Clinical Data to Power MRI Biomarker of Neonatal Brain Injury

摘要 该项目旨在发布我们最近收集的新生儿缺氧缺血的现有临床数据 脑病(HIE)。新生儿缺氧缺血性脑病影响1-5/1000的足月新生儿，是儿童早期出生的主要原因 死亡率和发病率。新生儿脑部磁共振成像(MRI)是临床常规获取的 照顾他。新生儿脑MRI有望揭示SO不良结局的3D神经解剖学机制 我们可以专门针对这些机制设计新的治疗方法。新生儿脑部核磁共振也带来了希望 识别那些有可能在以后的生活中出现不良后果的新生儿，以便及早干预 该计划可以针对那些高危新生儿，以最大限度地受益。尽管核磁共振在治疗HIE方面起着至关重要的作用，但 目前临床上的规范是由神经放射科专家或神经科专家直观地阅读MRI。专家读到， 然而，它有许多局限性--主观性、定性、不足以揭示机制，以及不足以 预测结果。随着人工智能(AI)的兴起，MRI的客观和定量分析成为可能 在医学和神经影像信息学方面。然而，一个主要的限制是缺乏公开可用的数据 他来了。我们的项目旨在通过归档和发布我们的临床获得的大规模(N=231)和 多个地点(2家医院)的HIE数据。我们的数据部分由NIH R01(2012-2017)和 基金会(2016-2020年)。我们的数据是全面的，包括临床数据元素(来自母亲和 新生儿)、新生儿脑MRI(结构和扩散序列)、专家共识病变注释 新生儿脑MRI、NICU结局(死亡/存活、住院时间)和2岁儿童神经认知的区域 结果(正常/不利，发育方面的是/否，听力/视觉/运动障碍的是/否，以及 脑性瘫痪的是/否)。我们的数据也很有代表性，来自不同种族/民族的患者 来自不同MRI扫描仪(西门子3T或GE 1.5T)的结果范围广泛的患者组， 不同的成像方案，核磁共振扫描的天数不同。我们还将从现有数据中派生出新数据 数据。匿名(取消识别)的数据将被发布到NCBI数据库数据库平台，其中包含 访问“选项，需要IRB和数据使用协议(DUA)。派生数据将通过以下命令发布到DBGaP “开放获取”选项，无需任何批准即可免费下载。这两种版本选项都与 已经在DBGaP上发布的其他临床和MRI数据。我们希望这第一个全面的数据 将推动人工智能未来的合作努力，以自动识别MRI中的HIE病变，并使人工智能准确 结合临床和MRI信息预测HIE预后。

Abstract This project aims to release our recently gathered existing clinically-acquired data for neonatal hypoxic ischemic encephalopathy (HIE). HIE affects 1-5/1000 term-born neonates and is a major cause of early-childhood mortality and morbidity. Neonatal brain magnetic resonance imaging (MRI) is acquired routinely for the clinical care of HIE. Neonatal brain MRI is expected to reveal 3D neuroanatomic mechanisms of adverse outcomes so that we can design new treatments specifically target those mechanisms. Neonatal brain MRI also carries hope to identify those neonates who are at risk to develop adverse outcomes later in life, so that early intervention program can target those at-risk neonates for maximum benefit. Despite MRI's vital role in caring for HIE, the current norm in clinical practice is to read MRI visually by expert neuroradiologist or neurologist. Expert reads, however, has many limitations – subjective, qualitative, insufficient to reveal mechanisms, and inadequate to predict outcomes. Objective and quantitative analysis of MRI is possible with the rise of artificial intelligence (AI) in medical and neuroimaging informatics. A major limitation, however, is the lack of publicly-available data on HIE. Our project aims to fill this gap, by archiving and releasing our clinically-acquired, large-scale (N=231), and multi-site (2 hospitals) data on HIE. Our data was acquired partly funded by NIH R01 (2012-2017) and foundations (2016-2020). Our data is comprehensive, including clinical data elements (from both mothers and neonates), neonatal brain MRI (structural and diffusion sequences), expert-consensus annotation of lesion regions in neonatal brain MRI, NICU outcome (death/survival, length of stay), and 2-year-old neurocognitive outcomes (normal/adverse, yes/no for development dealy, yes/no for the hearing/visual/motor impairment, and yes/no for cerebral palsy). Our data is also representative, coming from patients with different racial/ethnicity groups, in patients with a wide range of outcomes, from different MRI scanners (Siemens 3T or GE 1.5T), with different imaging protocols, and MRI scanned on different days of life. We will also derive new data from existing data. The anonymized (de-identified) data will be released to the NCBI dbGaP platform with the “Controlled Access” option, requiring IRB and data use agreement (DUA). The derived data will be released to dbGaP with the “Open Access” option, freely downloadable without any approval. Both release options are consistent with other clinical and MRI data that have already been released on dbGaP. We hope this first comprehensive data will boost future collaborative efforts for AI to automatically identify HIE lesions in MRI, and for AI to accurately predict HIE outcome integrating clinical and MRI information.