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: 10557147
• 负责人: Kenichi Oishi
• 金额: $ 41.43万
• 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/10557147
• 关键词: 2 year old; Adjuvant; Affect; Age Months; Algorithms; Anatomy; Area; Atlases; Basal Ganglia; Biochemical; Brain; Brain Injuries; Caregivers; Characteristics; Classification; Clinical; Cohort Studies; Data; Deformity; Detection; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Early Intervention; Educational process of instructing; Electroencephalography; Evaluation; Exposure to; Future; Goals; Growth; Image; Image Analysis; Infant; Label; Language; Lesion; Libraries; Link; Localized Lesion; Magnetic Resonance Imaging; Manuals; Methodology; Morphology; Motor; Neonatal; Neuroanatomy; 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; Technology; Testing; Translating; brain magnetic resonance imaging; classification algorithm; clinical phenotype; cohort; detection method; 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; 2 year old; Adjuvant; Affect; Age Months; Algorithms; Anatomy; Area; Atlases; Basal Ganglia; Biochemical; Brain; Brain Injuries; Caregivers; Characteristics; Classification; Clinical; Cohort Studies; Data; Deformity; Detection; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Early Intervention; Educational process of instructing; Electroencephalography; Evaluation; Exposure to; Future; Goals; Growth; Image; Image Analysis; Infant; Label; Language; Lesion; Libraries; Link; Localized Lesion; Magnetic Resonance Imaging; Manuals; Methodology; Morphology; Motor; Neonatal; Neuroanatomy; 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; Technology; Testing; Translating; brain magnetic resonance imaging; classification algorithm; clinical phenotype; cohort; detection method; 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.

朝着我们的长期目标，即在治疗新生儿缺氧治疗方面提供精确医学 脑病（HIE），我们计划开发一个方法学框架，以基于脑MRI进行分类 评估结合临床变量，以更好地预测神经系统预后。在此提案中，我们将 创建一个MRI定量工具来识别各种类型的病变，该病变与临床变量相结合， 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 新生儿时期获得的脑损伤原因。它可能导致广泛的神经系统并发症 这会影响各种功能域，并具有异质性严重程度。 HIE亚型和特定的分层 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 医疗提供者所必需的，以指导适当的资源分配。虽然预测 到目前为止，备受期待的生物标志物都没有得到验证。 MRI表现强大 在严重的MRI发现中，严重神经行为缺陷的预测能力。然而， 预测中等至上的变化甚至正常的大脑MRI之后的结果不会 确保正常的神经行为结局。随着图像分析技术的最新进展，我们 打算通过检测和量化中等数量来提高灵敏度和负预测价值 病理变化，难以定性评估。由于个性化的预测不能是 由单个功能制成，因为每个功能都与结果微弱相关，我们假设该患者 分层，结合大脑MRI特征和临床特征，将高度准确 预言。我们将应用开发的自动结构图像量化（SIQ）管道 并通过R01HD065955进行验证，以在本提案中用于MRI量化。 Hie队列 研究（R01HD086058）将提供一个由MRI组成的教学文件库， SIQ算法从中学习了病变的特征。该队列还包括临床变量， 例如血清标记和脑电图，结合MRI特征和测试数据 验证研究。对于AIM 1，我们将创建一个参考库，其中包括具有多种多样的MRI地图集 病理性的变化是由于Hie引起的。结合多ATLAS标签融合和病变定位算法， 该库可以实现强大的SIQ。对于AIM 2，我们将在MRI功能上应用监督的学习算法 由SIQ量化以识别脑病变和与某些结果相关的严重性。目标3 将对MRI特征和临床变量使用有监督的分类算法来确定HIE 与受影响的功能域和结果的严重程度有关的亚型。该项目将提供一个 使用的方法论框架来识别有发育风险的Hie婴儿的亚组 神经系统并发症，谁可能受益于当前和未来的早期干预措施。