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.

我们的长期目标是在新生儿缺氧缺血性治疗中提供精准医疗