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

• 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
• 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' s Disease; Alzheimer' 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

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.

项目摘要/摘要 阿尔茨海默病(AD)是一种常见且代价高昂的神经退行性疾病，其特点是长期 临床前阶段，包括AD的前驱阶段，也称为轻度认知障碍(MCI)。 许多但不是所有的MCI患者以不同的速度进展为AD痴呆。MCI患者中，晚期 与早期MCI患者相比，MCI患者进展为AD的速度更快：随着 失忆。随着潜在的疾病修改药物被测试其延缓AD痴呆症的能力，它 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。这将会 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 药物和安慰剂的区别。拟议的项目将响应PA-20-200：NIH小额研究资助计划 (不允许进行家长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MCI。 使用基线测量的AD痴呆转化，使用AD神经成像倡议(ADNI)的数据 学习。我们将使用美国国立卫生研究院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。现有的大多数深度生存模型都是针对右删失的 数据，但MCI到AD痴呆的转换是间隔审查的。当区间删失数据被分析时 使用为右删失数据开发的方法，存活率总是被高估，这导致 对阿尔茨海默病诊断的延迟。我们将开发单独的早期MCI和MCI预测模型 用脑脊液、正电子发射断层扫描、磁学等生物标志物检测晚期脑梗塞 磁共振成像(MRI)和临床措施。最近，几个新的生物标志物被发现 对这项研究感兴趣的广告。包括血浆磷酸化tau181(p-tau181)、p-tau217和 淀粉样蛋白β42与淀粉样蛋白40的比值及胶质纤维酸性蛋白(βfi)。进行性精神障碍 像阿尔茨海默病一样，大多数临床事件都与疾病的动态密切相关。在目标2中，我们将 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。已建成 在我们开发的区间删失数据的惩罚生存模型中，我们提出了 扩展该模型以利用纵向生物标记物数据来产生对未来更准确的预测 转换。生物标志物以及临床和人口学特征被显示为改进了模型 正确审查数据的性能。我们期待新的生存模型能够改进模型 对区间删失数据的预测与最新模型的比较。这个项目将会发展得最好 深度生存模型预测每个MCI亚组从MCI到AD的痴呆转化。这样做的结果 项目将提供对每个特征如何有助于预测MCI-to-AD的重要理解 痴呆症转化。