Multi-Modality Image Data Fusion and Machine Learning Approaches for Personalized Diagnostics and Prognostics of MCI due to AD

用于 AD 所致 MCI 个性化诊断和预后的多模态图像数据融合和机器学习方法

• 批准号: 10264079
• 负责人: Jing Li
• 金额: $ 121.97万
• 依托单位: MS TECHNOLOGIES CORPORATION
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10264079
• 关键词: 3-Dimensional; Address; Affect; Algorithms; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease model; Amyloid; Anatomy; Brain; Brain Injuries; Clinic; Clinical; Clinical Data; Clinical Decision Support Systems; Clinical Trials; Clinics and Hospitals; Complement; Data; Data Set; Dementia; Development; Diagnosis; Diagnostic; Dimensions; Disease; Disease Management; Early Diagnosis; Early Intervention; Early treatment; Enrollment; Family health status; Goals; Health Care Costs; Healthcare Systems; Image; Impaired cognition; Individual; Insurance Coverage; Joints; Knowledge; Machine Learning; Magnetic Resonance Imaging; Measurement; Mind; Modality; Modeling; Multimodal Imaging; Neurodegenerative Disorders; Paper; Pathology; Patient Selection; Patients; Phase; Physicians; Positron-Emission Tomography; Prognosis; Psychological Transfer; Public Health; Publications; Research; Resources; Safety; Small Business Technology Transfer Research; Statistical Data Interpretation; Structure; Surveys; System; Training; Ursidae Family; Validation; Visualization; Work; accurate diagnosis; base; clinical application; cost; data fusion; deep learning; deep learning algorithm; diagnostic platform; feature extraction; feature selection; fluorodeoxyglucose positron emission tomography; imaging modality; improved; learning algorithm; machine learning algorithm; mild cognitive impairment; multimodality; neuroimaging; novel; particle; patient population; personalized approach; personalized diagnostics; prognostic; prognostic model; public health relevance; search engine; targeted therapy trials; therapy development; trend

Alzheimer’s Disease (AD) is a devastating neurodegenerative disease. The greatest treatment potential lies in early stages before irreversible brain damage occurs. Early treatment requires early detection of the disease. Imaging holds great promise for capturing early signs of AD. This capability can be substantially strengthened by integrating neuroimages of different modalities that characterize brain structure and function from complementary aspects. However, although various machine learning (ML) algorithms have been developed to integrate multi-modality images for diagnosis and prognosis of AD, there is a lack of novel, robust, effective algorithms to address patient-wise missing modalities in the integration. In real clinical data, it is inevitable that some image modalities are unavailable to some patients due to high cost, insurance coverage, and safety constraints. Thus, the existing algorithms may only work for a small portion of patients who have complete modalities. This significantly reduces the access to advanced imaging-based diagnostic systems from the general patient population and in broad clinical settings. Because of the limited clinical utility, it is difficult to commercialize the existing ML algorithms into clinical systems/products, whereas the current imaging-based products on the market focus on single image modalities or image measurement, processing, visualization, and statistical analysis (without advanced ML capabilities). To fill the unmet market niche, this STTR Phase II project will develop the first-ever broadly-applicable clinical decision support system, Multi-neuroimaging for Detecting AD (Mind-AD), which can accommodate varying availability of image modalities across different patients to build classifiers and provide accurate diagnosis and prognosis of AD for each individual at the early MCI stage. Our Phase I has successfully demonstrated the feasibility of the Mind-AD system. At Phase II, we propose functional optimization and validation of Mind-AD in three aims. Aim 1 will optimize the accuracy and robustness of the diagnostic/prognostic models by integrating our Phase I IMTL model with efficient PSO feature selection. The integrated IMTL-PSO is very efficient in selecting optimal feature subsets to yield accurate, robust diagnostic/prognostic models especially on independent validation datasets. Aim 2 will develop a novel IMTL- DL (deep learning) model to integrate incomplete multi-modality volumetric images. While IMTL-PSO is based on features defined using anatomical knowledge of the brain, IMTL-DL extracts features in a data-driven manner. Aim 3 will integrate IMTL-PSO and IMTL-DL through decision fusion to best leverage their complementary, joint strength, and validate the resulting Mind-AD system using two independent datasets. Our project is significant because Mind-AD is the first early diagnostic/prognostic system for AD using advanced ML algorithms to integrate incomplete multi-modality image datasets. Mind-AD will facilitate early detection, early intervention, patient selection in drug trials targeting the early stage, and will help achieve these goals in in broad clinical settings due to the capability of accommodating varying availability of image modalities from different patients.

阿尔茨海默病（Alzheimer's Disease，AD）是一种严重的神经退行性疾病。最大的治疗潜力在于 在不可逆的脑损伤发生之前的早期阶段。早期治疗需要早期发现疾病。 影像学对于捕捉AD的早期迹象具有很大的希望。这一能力可以大大加强 通过整合不同模态的神经图像， 互补的方面。然而，尽管已经开发了各种机器学习（ML）算法， 为了整合多模态图像用于AD的诊断和预后，缺乏新颖的、稳健的、有效的 解决整合中患者缺失模态的算法。在真实的临床数据中， 由于高成本、保险范围和安全性，某些成像模态对于某些患者是不可用的 约束因此，现有的算法可能仅适用于一小部分具有完全性的患者。 方式。这大大减少了从医疗机构到先进的基于成像的诊断系统的访问。 一般患者人群和广泛的临床环境中。由于有限的临床效用， 将现有的ML算法商业化到临床系统/产品中，而目前基于成像的 市场上的产品集中在单一图像模态或图像测量、处理、可视化 和统计分析（没有高级ML功能）。为了填补未满足的市场利基，该STTR第二阶段 该项目将开发有史以来第一个广泛适用的临床决策支持系统，多神经成像， 检测AD（Mind-AD），其可以适应不同的图像模态的不同可用性。 患者建立分类器，并在早期为每个个体提供准确的AD诊断和预后 MCI期。我们的第一阶段已经成功地证明了Mind-AD系统的可行性。在第二阶段，我们 提出了Mind-AD在三个目标上的功能优化和验证。目标1将优化精度， 通过将我们的I期IMTL模型与有效的PSO特征相结合， 选择.集成的IMTL-PSO在选择最优特征子集方面非常有效， 诊断/预后模型，特别是独立验证数据集。Aim 2将开发一种新的IMTL- DL（深度学习）模型，用于整合不完整的多模态体积图像。虽然IMTL-PSO是基于 在使用大脑的解剖学知识定义的特征上，IMTL-DL以数据驱动的方式提取特征。 目标3将通过决策融合集成IMTL-PSO和IMTL-DL，以最好地利用它们的互补性， 强度，并使用两个独立的数据集验证所得的Mind-AD系统。我们的项目意义重大 因为Mind-AD是第一个使用先进ML算法的AD早期诊断/预后系统， 集成不完整多模态图像数据集。Mind-AD将有助于早期发现，早期干预， 在药物试验的早期阶段选择患者，并将有助于在广泛的临床中实现这些目标 由于能够适应来自不同患者的图像模态的不同可用性，