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

阿尔茨海默病(AD)是一种毁灭性的神经退行性疾病。最大的治疗潜力在于 在不可逆转的脑损伤发生之前的早期阶段。早期治疗需要及早发现这种疾病。 成像技术在捕捉阿尔茨海默病早期征兆方面大有可为。这种能力可以大大加强。 通过整合不同形式的神经图像来表征大脑的结构和功能 相辅相成的方面。然而，尽管已经开发了各种机器学习(ML)算法 将多模式图像综合起来用于AD的诊断和预后，目前缺乏新颖、稳健、有效的方法 算法，以解决患者智慧在整合中缺失的医疗模式。在真实的临床数据中，不可避免的是 由于高昂的费用、保险范围和安全性，一些患者无法使用某些成像设备 约束条件。因此，现有的算法可能只适用于一小部分已完成 医疗模式。这大大减少了从 在普通患者群体和广泛的临床环境中。由于临床应用的局限性，很难在临床上 将现有的ML算法商业化到临床系统/产品中，而当前基于成像的 市场上的产品侧重于单一图像形态或图像测量、处理、可视化、 和统计分析(没有高级ML功能)。为了填补尚未满足的利基市场，这一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，即多神经成像 检测AD(Mind-AD)，它可以适应不同类型的图像形态的不同可用性 患者建立分类器，并在早期为每个人提供准确的AD诊断和预后 MCI阶段。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 从三个方面提出了Mind-AD的功能优化和验证。目标1将优化精度和 通过将我们的第一阶段IMTL模型与高效的PSO特征相结合来提高诊断/预测模型的稳健性 选择。集成的IMTL-PSO在选择最优特征子集方面非常有效，以产生准确、健壮的结果 诊断/预后模型，特别是在独立验证数据集上。AIM 2将开发一种新的IMTL- 融合不完整多模体图像的深度学习模型。而IMTL-PSO是基于 在使用大脑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。 AIM 3将通过决策融合将IMTL-PSO和IMTL-DL整合在一起，以最大限度地利用它们的互补、联合 强度，并使用两个独立的数据集验证得到的Mind-AD系统。我们的项目意义重大 因为Mind-AD是第一个使用高级ML算法对AD进行早期诊断/预测的系统 整合不完整的多通道图像数据集。Mind-AD将有助于早期发现、早期干预、 针对早期阶段的药物试验中的患者选择，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不同成像方式的可用性，因此可以在不同的环境中使用。

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.