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.

朝着我们在新生儿缺氧缺血性脑病治疗中提供精准药物的长期目标迈进 因此，我们计划开发一种基于脑MRI的方法学框架来分类HIE 评估结合临床变量，以更好地预测神经预后。在本提案中，我们将 创建MRI量化工具，以识别各种类型的病变，结合临床变量， 将分离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 影响不同功能领域的疾病，严重程度不同。新生儿缺氧缺血性脑病亚型和特异性 对于开发和提供有针对性的辅助治疗和康复治疗， 为医疗服务提供者提供必要的信息，以指导资源的适当分配。虽然预测 生物标志物一直被寄予厚望，但迄今为止，还没有一个得到验证。核磁共振显示 在严重MRI结果的背景下，严重神经行为缺陷的预测能力。然而，在这方面， 预测中度至轻度变化或甚至正常的脑部MRI检查结果并不 保证正常的神经行为随着图像分析技术的发展，我们 旨在通过检测和量化中度至轻度的 病理变化，难以定性评价。由于个体化预测不可能 从一个单一的功能，因为每个功能与结果弱相关，我们假设，病人 结合脑MRI特征和临床特点进行分层，将具有高度的准确性， 预测.我们将应用我们的自动化逐结构图像量化（SIQ）管道，开发 并通过R01HD 065955进行了确认，用于本提案中的MRI定量。HIE队列 研究（R01HD 086058）将提供一个教学文件库，其中包含各种类型病变的MRI， 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图，结合MRI特征和测试数据， 验证研究对于目标1，我们将创建一个参考库，其中包括具有各种 病理改变。结合多图谱标签融合和病灶定位算法， 该库实现了鲁棒的SIQ。对于目标2，我们将对MRI特征应用监督学习算法 通过SIQ量化，以识别脑损伤和与某些结果相关的严重程度。目标3 将使用MRI特征和临床变量的监督分类算法来确定HIE 与受影响的功能领域和结果的严重程度相关的亚型。该项目将提供一个 用于识别有发展风险的HIE婴儿亚组的方法框架 神经系统并发症，以及可能从当前和未来的早期干预中受益的患者。