Personalized risk assessment in Neurofibromatosis Type 1

项目总结/摘要 神经纤维瘤病（NF）包括一组复杂的遗传性疾病，影响几乎每个器官系统 并增加良性和恶性中枢和外周神经系统肿瘤的发展风险。的 在这三种类型的NF中，1型神经纤维瘤病（NF 1）是最常见的，约1/ 每3，000个新生儿中就有一个没有种族、性别或民族偏好的婴儿。虽然NF 1是在完全渗透剂中遗传的， 常染色体显性方式，有广泛的个体间变异方面的临床特征和他们的 影响患者发病率。临床异质性是临床医生和家庭面临的普遍挑战， 儿童和成人NF 1的治疗在很大程度上仍然是反应性的，没有可靠的生物标志物 或用于诊断时的早期风险分层和/或预后评估的预测模型。 传统的方法，重点是确定一个单一的临床或生物标志物，可以测量 用于评估NF 1的疾病风险或轨迹，取得了有限的成功，并阻碍了进展 为受NF 1影响的个体开发精准医学。为了应对这些挑战， 有机会改善NF 1患者的护理，我们的目标是验证和验证替代方案， 开发基于人工智能（AI）的NF 1临床决策支持工具的可推广方法 亚表型，在两个临床研究中心以比较的方式实施和评价。 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 整合和协调来自多个来源的数据的算法，例如临床数据库、结构化 电子健康记录和非结构化临床笔记。其次，我们将开发基于AI的管道 能够生成预测模型和工具，以确定三个关键NF 1亚群的疾病风险， 表型（OPG、脊柱侧凸和ADHD）。然后，我们将评估模型的定量准确性， 在NF 1临床医生的帮助下，在护理点的临床可操作性。最后，我们将验证这些方法 和模型，以便我们能够更好地了解推广和 基于不同的医疗保健系统、环境和人群传输这样的预测模型。 我们预计，使用人工智能技术，以研究NF 1特异性亚表型， 两个不同的网站将产生有关以下方面的新颖且可能具有临床可操作性和可推广性的见解： NF 1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.