Precision Medicine for Neonatal Hypoxic-Ischemic Encephalopathy: Combined Neuroimaging Clinical Approach to Link Phenotypes to Prognosis

新生儿缺氧缺血性脑病的精准医学：将表型与预后联系起来的联合神经影像学临床方法

• 批准号: 10417856
• 负责人: Kenichi Oishi
• 金额: $ 42万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10417856
• 关键词: 2 year old; Adjuvant; Affect; Age-Months; Algorithms; Anatomy; Area; Atlases; Basal Ganglia; Biochemical; Brain; Brain Injuries; Caregivers; Characteristics; Clinical; Cohort Studies; Data; Deformity; Detection; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Early Intervention; Educational process of instructing; Electroencephalogram; Evaluation; Exposure to; Future; Goals; Growth; Image; Image Analysis; Infant; Label; Language; Lesion; Libraries; Link; Localized Lesion; Magnetic Resonance Imaging; Manuals; Methodology; Methods; Morphology; Motor; Neonatal; Neurologic; Neurological outcome; Outcome; Pathologic; Performance; Phenotype; Predictive Value; Premature Birth; Prognosis; Protocols documentation; Provider; Qualitative Evaluations; Rehabilitation therapy; Resource Allocation; Risk; Scanning; Sensitivity and Specificity; Serum Markers; Severities; Signal Transduction; Specificity; Stratification; Structure; Subgroup; Supervision; Technology; Testing; Translating; base; brain magnetic resonance imaging; classification algorithm; clinical phenotype; cohort; functional outcomes; imaging modality; infant outcome; interest; learning algorithm; medically necessary care; neonatal brain; neonatal hypoxic-ischemic brain injury; neonatal period; neonate; neurobehavioral; neuroimaging; outcome prediction; patient stratification; personalized predictions; precision medicine; predictive marker; prenatal exposure; supervised learning; targeted delivery; tool; validation studies

Toward our long-term goal of delivering precision medicine in the treatment of neonatal hypoxic-ischemic encephalopathy (HIE), we plan to develop a methodological framework to classify HIE based on brain MRI evaluation combined with clinical variables to better predict neurological prognosis. In this proposal, we will create an MRI quantification tool to identify various types of lesions, which, combined with clinical variables, will isolate HIE subtypes and subsequent clinical phenotypes to predict prognosis. HIE is the most common cause of acquired brain injury in the neonatal period. It can result in a wide range of neurological complications that affect various functional domains, with heterogeneous severity. Stratification of HIE subtypes and specific prognoses is essential for developing and delivering targeted adjuvant and rehabilitative treatments and is also necessary for medical providers in order to guide the appropriate allocation of resources. Although predictive biomarkers have been highly anticipated, as of yet, there are none validated. MRI has demonstrated strong predictive power for severe neurobehavioral deficits within the context of severe MRI findings. However, predicting outcomes following moderate-to-mild changes or even a normal-looking brain MRI does not guarantee normal neurobehavioral outcomes. With the recent advances in image analysis technologies, we intend to increase the sensitivity and negative predictive value by detecting and quantifying moderate-to-mild pathological changes, which are difficult to evaluate qualitatively. Since individualized prediction cannot be made from a single feature, as each feature weakly correlates with outcomes, we hypothesize that patient stratification, combining brain MRI features and clinical characteristics, will be highly accurate for individualized prediction. We will apply our automated structure-by-structure image quantification (SIQ) pipeline, developed and validated through R01HD065955, to be applied for the MRI quantification in this proposal. The HIE cohort study (R01HD086058) will provide a library of teaching files that consist of MRIs with various types of lesions, from which the SIQ algorithm learns the features of the lesions. The cohort also includes clinical variables, such as serum markers and electroencephalograms, combined with the MRI features and test data for the validation study. For Aim 1, we will create a reference library that includes MRI atlases with various pathological changes due to HIE. Combined with the multi-atlas label fusion and lesion localization algorithms, the library enables a robust SIQ. For Aim 2, we will apply a supervised learning algorithm to the MRI features quantified by the SIQ to identify brain lesions and the severity that is associated with certain outcomes. Aim 3 will use a supervised classification algorithm for the MRI features and clinical variables to determine the HIE subtypes related to the affected functional domains and the severity of the outcomes. This project will provide a methodological framework with which to identify subgroups of infants with HIE who are at risk of developing neurological complications, and who may benefit from current and future early interventions.

迈向精准用药治疗新生儿缺氧缺血性脑病的长期目标 脑病(HIE)，我们计划开发一种基于脑MRI的HIE分类方法框架 结合临床变量进行评估，更好地预测神经预后。在这项提案中，我们将 创建MRI量化工具来识别各种类型的病变，结合临床变量， 将分离出HIE亚型和随后的临床表型以预测预后。HIE是最常见的 新生儿期获得性脑损伤的原因。它会导致广泛的神经系统并发症。 影响各种功能领域，具有不同的严重性。新生儿缺氧缺血性脑病亚型和特异性的分层 预后对于开发和提供有针对性的辅助和康复治疗至关重要，也是 有必要为医疗服务提供者提供指导，以便适当分配资源。尽管具有预测性 生物标记物被高度期待，但到目前为止，还没有得到证实的。核磁共振显示出很强的 在严重MRI表现的背景下，对严重神经行为缺陷的预测能力。然而， 预测中度到轻度改变后的结果，甚至是看起来正常的脑部核磁共振检查也不能 保证正常的神经行为结果。随着图像分析技术的最新进展，我们 旨在通过检测和量化中、轻度疾病来提高敏感性和阴性预测值 病理改变，难以定性评估。由于个性化预测不能 由单个特征组成，因为每个特征与结果的相关性很弱，我们假设患者 结合脑部MRI特征和临床特征的分层将对个性化具有很高的准确性 预测。我们将应用我们开发的自动逐结构图像量化(SIQ)流水线 并通过R01HD065955进行了验证，应用于本方案中的MRI量化。他的队列 研究(R01HD086058)将提供一个教学文件库，其中包括具有各种类型病变的磁共振成像， SIQ算法从中学习病变的特征。队列还包括临床变量， 如血清标志物和脑电，结合MRI特征和测试数据 验证性研究。对于目标1，我们将创建一个参考库，其中包括各种不同的MRI地图集 新生儿缺氧缺血性脑病的病理改变。结合多图谱标记融合和病变定位算法， 该库实现了健壮的SIQ。对于目标2，我们将把有监督的学习算法应用于MRI特征 由SIQ量化，以确定与某些结果相关的脑损伤和严重程度。目标3 将使用监督分类算法对MRI特征和临床变量确定HIE 与受影响的功能域和预后严重程度相关的亚型。该项目将提供一个 确定有发展风险的新生儿缺氧缺血性脑病亚群的方法学框架 神经并发症，以及谁可能受益于当前和未来的早期干预。