Personalized risk assessment in Neurofibromatosis Type 1

项目摘要/摘要 神经纤维瘤病包括一组复杂的遗传性疾病，几乎影响到每个器官系统。 并增加发生良性和恶性中枢和外周神经系统肿瘤的风险。的 三种类型的神经纤维瘤病，神经纤维瘤病1型(NF1)是最常见的，大约发生在 每3,000名新生儿中，没有种族、性别或民族偏见。而NF1是在完全渗透剂中遗传的 常染色体显性遗传方式，在临床特征和他们的 对患者发病率的影响。临床异质性是临床医生和家庭普遍面临的挑战，因为 儿童和成人NF1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 或在诊断时用于早期风险分层和/或预后评估的预测模型。 传统方法，侧重于识别可以测量的单个临床或生物标记物 并用于评估NF1的疾病风险或轨迹，取得的成功有限，阻碍了进展 在为受NF1影响的个人开发精准医学方面。为了应对这些挑战，并与 有机会改善对NF1患者的护理，我们的目标是验证和验证替代方案和 为NF1开发基于人工智能(AI)的临床决策支持工具的通用方法 亚表型，在两个临床地点以比较的方式实施和评估。 我们提出的项目将首先使用基于文本挖掘的临床表型来生成多尺度数据集 集成和协调来自多个来源的数据的算法，例如结构化的临床数据库 电子健康记录和非结构化的临床记录。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，以确定三个关键NF1亚型的疾病风险 表型(OPGs、脊柱侧弯和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，护理点的临床可操作性。最后，我们将对这些方法进行验证 和模型，这样我们就可以更好地了解推广和 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特定的亚型将在 两个不同的站点将产生新的、潜在的临床可操作和可推广的关于 对NF1患者的精确诊断和护理，具有更广泛的适用性 复杂的疾病状态。

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.