Personalized risk assessment in Neurofibromatosis Type 1

1 型神经纤维瘤病的个性化风险评估

基本信息

批准号：
10621489
负责人：
Aditi Gupta
金额：
$ 59.67万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10621489
关键词：
Adult Affect Algorithms Artificial Intelligence Attention deficit hyperactivity disorder Automobile Driving Behavioral Benign Biological Markers Biological Models Birth Caring Central Nervous System Characteristics Child Clinical Clinical Data Clinical Markers Collaborations Complex Data Data Set Development Diagnosis Disease Disease Management Disease Surveillance Electronic Health Record Environment Ethnic Origin Family Family member Genetic Diseases Germ-Line Mutation Glioma Healthcare Systems Individual Informatics Inherited Institution Knowledge Longevity Machine Learning Malignant - descriptor Manuals Measurable Measures Methods Modeling Morbidity - disease rate NF1 gene Neurofibromatoses Neurofibromatosis 1 Optics Outcome Pathway interactions Patients Pattern Pediatric Hospitals Performance Peripheral Nervous System Neoplasms Phenotype Physicians Population Predisposition Preventive measure Prognostic Marker Psychometrics Quality of life Race Registries Reproducibility Research Risk Risk Assessment Safety Site Structure Symptoms Syndrome Techniques Time Universities Washington artificial intelligence method autosome behavioral phenotyping body system burden of illness care outcomes care providers clinical database clinical decision support clinical decision-making clinical heterogeneity clinical phenotype clinical research site clinically actionable comparative cost data harmonization deep learning disease phenotype disease prognosis disorder risk electronic structure gene interaction improved individual patient insight inter-individual variation interest machine learning model multiple data sources multiscale data novel phenotyping algorithm point of care precision medicine predictive modeling predictive tools prognostic response risk stratification scoliosis sex structured data success support tools text searching tool tumor verification and validation

项目摘要

Project Summary/Abstract Neurofibromatosis (NF) encompasses a set of complex genetic disorders that affect almost every organ system and increase risk for the development of benign and malignant central and peripheral nervous system tumors. Of the three types of NF, Neurofibromatosis Type 1 (NF1) is the most prevalent occurring in approximately 1 in every 3,000 births without predilection for race, sex, or ethnicity. While NF1 is inherited in a fully penetrant autosomal dominant manner, there is wide inter-individual variability with respect to clinical features and their impact on patient morbidity. Clinical heterogeneity is a pervasive challenge for clinicians and families, as the management of children and adults with NF1 remains largely reactive, without reliable biomarkers or predictive models for early risk stratification and/or prognostic assessment at the time of diagnosis. Traditional approaches, which focus on identifying a single clinical or biological marker that can be measured and used to assess disease risk or trajectory in NF1, have achieved limited success and have hindered progress in the development of precision medicine for NF1-affected individuals. In response to these challenges, and with the opportunity to improve the care of individuals with NF1, we aim to verify and validate an alternative and generalizable approach for developing artificial intelligence (AI)-based clinical decision support tools for NF1 sub-phenotypes, implemented and evaluated in a comparative manner across two clinical sites. Our proposed project will first generate a multi-scale data set using a text-mining based clinical phenotyping algorithm to integrate and harmonize data from multiple sources such as clinical databases, structured electronic health records, and unstructured clinical notes. Secondly, we will develop AI-based pipelines capable of generating predictive models and tools to identify disease risk for three critical NF1 sub- phenotypes (OPGs, scoliosis, and ADHD). We will then evaluate the models for quantitative accuracy and clinical actionability at the point of care with the help of NF1 clinicians. Finally, we will validate these methods and models across multiple sites, so that we can better understand the challenges to generalizing and transporting such predictive models based across different healthcare systems, environments, and populations. We anticipate that the use of artificial intelligence techniques in order to study NF1-specific sub-phenotypes at two different sites will yield novel and potentially clinically-actionable and generalizable insights concerning the precision diagnosis and care of individuals with NF1, with broader applicability across a spectrum of similarly complex disease-states.

项目概要/摘要神经纤维瘤病 (NF) 包含一系列复杂的遗传性疾病，几乎影响每个器官系统并增加发生良性和恶性中枢和周围神经系统肿瘤的风险。的在 NF 的三种类型中，1 型神经纤维瘤病 (NF1) 最常见，发生在大约 1 岁以下每 3,000 名新生儿中就有一个不分种族、性别或民族。虽然 NF1 是在完全渗透性中遗传的常染色体显性遗传方式，在临床特征及其方面存在广泛的个体差异对患者发病率的影响。临床异质性是临床医生和家庭面临的普遍挑战，因为患有 NF1 的儿童和成人的治疗在很大程度上仍然是被动的，没有可靠的生物标志物或诊断时早期风险分层和/或预后评估的预测模型。传统方法侧重于识别可测量的单一临床或生物标志物并用于评估 NF1 的疾病风险或轨迹，取得的成功有限并阻碍了进展为 NF1 患者开发精准医疗。为了应对这些挑战，并有机会改善 NF1 患者的护理，我们的目标是验证和验证替代方案用于开发基于人工智能 (AI) 的 NF1 临床决策支持工具的通用方法亚表型，在两个临床中心以比较方式实施和评估。我们提出的项目将首先使用基于文本挖掘的临床表型生成多尺度数据集用于集成和协调来自多个来源（例如临床数据库、结构化数据）的数据的算法电子健康记录和非结构化临床记录。其次，我们将开发基于人工智能的管道能够生成预测模型和工具来识别三种关键 NF1 亚型的疾病风险表型（OPG、脊柱侧凸和 ADHD）。然后我们将评估模型的定量准确性和在 NF1 临床医生的帮助下，在护理点进行临床操作。最后，我们将验证这些方法以及跨多个站点的模型，以便我们能够更好地理解泛化和模型化所面临的挑战跨不同的医疗保健系统、环境和人群传输此类预测模型。我们预计，使用人工智能技术来研究 NF1 特异性亚表型两个不同的网站将产生新颖的、潜在的临床可行的和可推广的见解对 NF1 患者进行精确诊断和护理，在一系列类似的疾病中具有更广泛的适用性复杂的疾病状态。