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

多部位临床数据为新生儿脑损伤的 MRI 生物标志物提供动力

• 批准号: 10391525
• 负责人: Yangming Ou
• 金额: $ 9.49万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-12 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10391525
• 关键词: 2 year old; 3-Dimensional; Address; Affect; Age; Agreement; Alzheimer' s Disease; Archives; Artificial Intelligence; Atlases; Authorization documentation; Biological Markers; Brain; Brain Injuries; Brain Neoplasms; Caring; Cerebral Palsy; Cessation of life; Child Health; Clinical; Clinical Data; Clinical Trials; Collection; Consensus; Consumption; Data; Data Element; Data Set; Databases; Detection; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Early Intervention; Ethnic group; Foundations; Frequencies; Funding; Future; Grant; Hearing; Hospitals; Human Development; Image; Informatics; Injury; Institutional Review Boards; International; Knowledge; Length of Stay; Lesion; Life; Link; MRI Scans; Magnetic Resonance Imaging; Maps; Medical; Medical Imaging; Metadata; Morbidity - disease rate; Mothers; Motor; National Institute of Child Health and Human Development; Neonatal; Neonatal Brain Injury; Neonatal Intensive Care Units; Neurocognitive; Neurocognitive Deficit; Neurologist; Outcome; Outcome Measure; Patient-Focused Outcomes; Patients; Pattern; Population; Prognosis; Protocols documentation; Race; Reader; Reporting; Research; Risk; Role; Sample Size; Signal Transduction; Site; Standardization; Structure; Study of magnetics; Therapeutic; Time; Treatment outcome; United States National Institutes of Health; Visual; adverse outcome; base; brain magnetic resonance imaging; clinical care; clinical practice; clinical research site; cohort; cranium; data access; data de-identification; database of Genotypes and Phenotypes; design; early childhood; improved; improved outcome; intervention program; magnetic resonance imaging biomarker; meetings; mortality; motor impairment; natural hypothermia; neonatal brain; neonatal hypoxic-ischemic brain injury; neonatal outcome; neonate; neuroimaging; novel; outcome prediction; patient registry; response; sex; success; symposium; targeted treatment; visual motor

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.

摘要 该项目旨在发布我们最近收集的现有新生儿缺氧缺血性脑病临床数据， 脑病（HIE）。HIE影响1-5/1000的足月新生儿，并且是幼儿期的主要原因 死亡率和发病率。新生儿脑磁共振成像（MRI）是常规获取的临床 照顾你的孩子。新生儿脑MRI有望揭示不良结局的3D神经解剖机制， 我们可以设计出专门针对这些机制的新疗法。新生儿大脑MRI也带来了希望 识别那些有风险在以后的生活中出现不良后果的新生儿， 该计划可以针对那些有风险的新生儿，以获得最大的好处。尽管MRI在治疗HIE中发挥着重要作用， 临床实践中的当前规范是由神经放射科专家或神经科专家视觉读取MRI。专家解读， 然而，它有许多局限性--主观的、定性的、不足以揭示机制的， 预测结果。随着人工智能（AI）的兴起，MRI的客观定量分析成为可能 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关于以下方面的公开数据： 嗨。我们的项目旨在填补这一空白，通过存档和发布我们的临床获得的，大规模的（N=231）， HIE的多中心（2家医院）数据。我们的数据部分由NIH R 01（2012-2017）资助， 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（结构和弥散序列）、专家一致的病变注释 新生儿脑MRI区域、NICU结局（死亡/存活、住院时间）和2岁儿童神经认知功能 结局（正常/不良，发育迟缓是/否，听力/视觉/运动障碍是/否，以及 脑瘫是/否）。我们的数据也具有代表性，来自不同种族/民族的患者 不同MRI扫描仪（Siemens 3 T或GE 1.5T）的患者，结局范围广泛， 不同的成像协议，以及在不同的生命期进行MRI扫描。我们还将从现有的数据中获得新的数据。 数据匿名（去识别）数据将通过“受控” 访问”选项，需要IRB和数据使用协议（DUA）。导出的数据将发布到dbGaP， “开放获取”选项，无需任何批准即可免费下载。两种版本选项均符合 dbGaP上已经发布的其他临床和MRI数据。我们希望这第一个全面的数据 这将促进未来的合作努力，使人工智能能够在MRI中自动识别HIE病变，并使人工智能能够准确地 结合临床和MRI信息预测HIE预后。

项目成果

Global-Local Transformer for Brain Age Estimation.

• DOI: 10.1109/tmi.2021.3108910
• 发表时间: 2022-01
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6

Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan.

• DOI: 10.1016/j.media.2021.102091
• 发表时间: 2021-08
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: He S;Pereira D;David Perez J;Gollub RL;Murphy SN;Prabhu S;Pienaar R;Robertson RL;Ellen Grant P;Ou Y
• 通讯作者: Ou Y

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.

• DOI: 10.3389/fnagi.2023.1249415
• 发表时间: 2023
• 期刊: Frontiers in aging neuroscience
• 影响因子: 4.8