Application of deep learning and novel survival models to predict MCI-to-AD dementia progression

项目概要/摘要 阿尔茨海默病（AD）是一种常见且昂贵的神经退行性疾病，其特征在于长时间的阿尔茨海默病（AD）。 临床前阶段，包括AD的前驱期，也称为轻度认知障碍（MCI）。 许多但不是所有MCI患者以不同的速度进展为AD痴呆。在MCI患者中，晚期 MCI患者比早期MCI患者更快地进展为AD： 失忆随着潜在的疾病修饰药物被测试其延迟AD痴呆的能力， 变得至关重要的工具，可以更准确地预测MCI到AD痴呆症的转换。这将 允许选择研究期间最有可能下降的队列，最大限度地提高检测 药物/安慰剂差异。PA-20-200：NIH小型研究资助计划 （不允许进行母R 03临床试验）。在目标1中，我们将开发新的深度生存模型来预测MCI到 使用AD神经影像学倡议（ADNI）的数据，使用基线测量的AD痴呆转换 study.我们将使用NIH资助的神经变性和转化神经科学中心的数据 （CNTN）作为测试数据。大多数现有的深度生存模型都是针对右删失开发的 数据，但MCI到AD痴呆的转换是区间删失的。当区间删失数据被分析时， 使用为右删失数据开发的方法，生存率总是被高估， AD痴呆诊断的延迟。我们将为早期MCI开发单独的预测模型， 晚期MCI与来自脑脊液（CSF）、正电子发射断层扫描（PET）、磁共振成像（MRI） 核磁共振成像（MRI）和临床测量。最近，发现了几种新的生物标志物， 本研究所关注的AD。这些包括血浆磷酸化-tau 181（p-tau 181）、p-tau 217和p-tau 217。 淀粉样蛋白β 42和淀粉样蛋白β 40的比例以及胶质纤维酸性蛋白（GFAP）。进行性疾病 像AD一样，大多数临床事件与疾病的动力学密切相关。在目标2中，我们 为具有时变纵向生物标志物数据的区间删失数据开发新的生存模型。建造 在我们开发的基于基线测量的区间删失数据的惩罚生存模型上，我们建议： 扩展该模型以利用纵向生物标志物数据来产生关于未来的更准确的预测， 转换.生物标志物与临床和人口统计学特征一起沿着被证明可以改善模型 右删失数据的性能。我们期望新的生存模型能够改进模型 与最先进的模型相比，区间删失数据的预测。该项目将开发最佳 深度生存模型预测每个MCI亚组的MCI至AD痴呆转化。的结果 项目将提供每个功能如何有助于预测MCI到AD的重要理解 痴呆症的转化

Project summary/Abstract Alzheimer's disease (AD) is a common and costly neurodegenerative disease that is characterized by a long pre-clinical stage, including a prodromal stage of AD also referred to as mild cognitive impairment (MCI). Many, but not all, MCI patients progress to AD dementia at varying rates. Among MCI patients, late stage MCI patients progress to AD faster than early stage MCI patients: a faster annual cognitive decline with loss of memory. As potential disease modifying drugs are tested for their ability to delay AD dementia, it becomes critical to have tools that can better accurately predict MCI-to-AD dementia conversion. This would allow selection of cohorts most likely to decline during the study period, maximizing the ability to detect a drug/placebo difference. The proposed project will respond to PA-20-200: NIH Small Research Grant Program (Parent R03 Clinical Trial Not Allowed). In Aim 1, we will develop new deep survival models to predict MCI-to- AD dementia conversion using baseline measures, by using data from the AD Neuroimaging Initiative (ADNI) study. We will use data from the NIH funded Center for Neurodegeneration and Translational Neuroscience (CNTN) as the test data. The majority of the existing deep survival models were developed for right censored data, but MCI-to-AD dementia conversion is interval censored. When interval censored data are analyzed by using the methods developed for right censored data, the survival rates are always over-estimated that leads to the delay in AD dementia diagnosis. We will develop separate prediction models for early stage MCI and late stage MCI with biomarkers from cerebrospinal fluid (CSF), positron emission tomography (PET), magnetic resonance imaging (MRI), and clinical measures. Recently, several new biomarkers have been discovered for AD that are of interest to this study. These include plasma phosphorylated-tau181 (p-tau181), p-tau217, and the ratio of amyloid-β 42 and amyloid-β 40, and glial fibrillary acidic protein (GFAP). In progressive disorders like AD, most clinical events are very strongly correlated with the dynamics of the disease. In Aim 2, we will develop novel survival models for interval-censored data with time-varying longitudinal biomarker data. Built on our developed penalized survival model for interval censored data using baseline measures, we propose to extend that model to leverage longitudinal biomarker data to produce more accurate predictions about future conversion. Biomarkers along with clinical and demographic features were shown to improve the model performances for right censored data. We expect that the new survival models will be able to improve model prediction for interval censored data as compared to state-of-the-art models. This project will develop optimal deep survival models to predict MCI-to-AD dementia conversion for each MCI subgroup. The results of this project will provide important understanding of how each feature contributes to prediction of MCI-to-AD dementia conversion.