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

应用深度学习和新型生存模型预测 MCI 至 AD 痴呆的进展

基本信息

批准号：
10725359
负责人：
Guogen Shan
金额：
$ 8.61万
依托单位：
UNIVERSITY OF FLORIDA
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：
2023-09-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10725359
关键词：
Alzheimer&apos s Disease Alzheimer&apos s disease patient Alzheimer’s disease biomarker Amyloid beta-Protein Biological Markers Cerebrospinal Fluid Clinical Clinical Data Clinical Research Clinical Trials Data Dementia Diagnosis Disease Event Funding Future Glial Fibrillary Acidic Protein Impaired cognition Individual Magnetic Resonance Imaging Measures Memory Loss Methods Modeling Modernization Nerve Degeneration Neurodegenerative Disorders Outcome Parents Participant Patient Care Patients Performance Pharmaceutical Preparations Phosphorylation Placebos Plasma Play Positron-Emission Tomography Process Property Reporting Research Project Grants Risk Factors Sampling Speed Subgroup Survival Rate Techniques Testing Time United States National Institutes of Health Validation Visit cohort cost deep learning drug development drug testing falls follow-up hazard high risk improved indexing interest mild cognitive impairment neuroimaging nonalzheimer dementia novel potential biomarker pre-clinical predictive modeling programs research study tau Proteins tau-1 theories time interval tool translational neuroscience web site

项目摘要

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.

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，其特点是长期病程临床前阶段，包括 AD 的前驱阶段，也称为轻度认知障碍 (MCI)。许多（但不是全部）MCI 患者以不同的速度进展为 AD 痴呆，其中 MCI 患者处于晚期。 MCI 患者进展为 AD 的速度比早期 MCI 患者更快：年度认知能力下降更快随着潜在的疾病缓解药物延缓 AD 痴呆的能力被测试，它会导致记忆丧失。拥有能够更准确地预测 MCI 到 AD 痴呆症转化的工具变得至关重要。允许选择在研究期间最有可能下降的队列，最大限度地提高检测到的能力拟议项目将响应 PA-20-200：NIH 小型研究资助计划。（不允许进行母体 R03 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 神经影像倡议 (ADNI) 的数据，使用基线测量进行 AD 痴呆症转化我们将使用来自 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为右删失开发的。数据，但 MCI 到 AD 痴呆的转换是区间删失的。使用为正确审查数据开发的方法，生存率总是被高估，导致我们将为早期 MCI 和 AD 痴呆症开发单独的预测模型。晚期 MCI，具有来自脑脊液 (CSF)、正电子发射断层扫描 (PET)、磁力的生物标志物最近，已经发现了几种新的生物标志物。本研究感兴趣的 AD 包括血浆磷酸化 tau181 (p-tau181)、p-tau217 和进行性疾病中淀粉样蛋白-β 42 和淀粉样蛋白-β 40 以及胶质纤维酸性蛋白 (GFAP) 的比率。与 AD 一样，大多数临床事件与疾病的动态密切相关，在目标 2 中，我们将这样做。使用构建的时变纵向生物标志物数据开发新颖的间隔删失数据生存模型。根据我们开发的使用基线测量的区间删失数据的惩罚生存模型，我们建议扩展该模型以利用纵向生物标记数据来对未来进行更准确的预测生物标志物以及临床和人口统计特征被证明可以改进模型。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，对区间删失数据进行预测。该项目将开发最佳模型。预测每个 MCI 亚组的 MCI 向 AD 痴呆转化的深度生存模型。项目将提供对每个特征如何有助于 MCI 到 AD 预测的重要理解痴呆症转化。