Monitoring Alzheimer’s Disease Progression in Mild Cognitive Impairment Stage Using Machine Learning-Based FDG-PET Classification Methods

Abstract

Background: We previously introduced a machine learning-based Alzheimer’s Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects. Objective: We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD. Methods: MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior. Results: The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7–0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01). Conclusion: These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD.

Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging

“…The early detection of AD is of paramount importance for effective patient management and prognostication. PET tracers for glucose metabolism [8,[22][23][24], amyloid [25,26], tau [27][28][29][30][31][32], and neuroinflammation imaging [33][34][35][36][37][38][39], as well as MRI techniques such as arterial spin labeling (ASL) [40][41][42], resting-state fMRI and task-related fMRI [43][44][45][46][47][48][49][50][51][52], multi-nuclear MRI [53,54], and chemical exchange saturation transfer (CEST) [55,56], have provided valuable insights into the pathological mechanisms of AD in patients (Table 1). Synapse loss is a major pathological change in AD, but its relationship to functional and structural connectivity dysfunction remains unclear.…”

A Comprehensive Review of Treatment Strategies for Early Avascular Necrosis

“…Adjustments to treatment plans can be made based on observed changes, ensuring that interventions remain aligned with the evolving nature of the disease. Regular monitoring enhances the ability to detect subtle alterations in joint structure and function, enabling timely interventions to mitigate disease progression [ 21 ].…”

Translating the future: image-to-image translation for the prediction of future brain metabolism

“…Monitoring an individual's [ 18 F]-FDG PET scans over the time course of several years allows to track changes in brain function and thus, to detect subtle alterations that might indicate early stages or progression of AD. 4 However, a diagnosis plan that involves regular PET scanning poses challenges, including increased radiation exposure, high cost, and patient burden. Computational models enabling to foresee individual trajectories of brain metabolism could counter this pitfall and thus, significantly impact personalized treatment strategies, early disease management, and therapy control in AD.…”

“…1,2 This information plays a vital role in the differential diagnosis 3 and monitoring of AD. 4 Brain positron emission tomography (PET) with [ 18 F]Fluorodeoxyglucose ([ 18 F]-FDG) is a powerful imaging modality that provides insights into brain metabolism by measuring neuronal dysfunction. Monitoring an individual's [ 18 F]-FDG PET scans over the time course of several years allows to track changes in brain function and thus, to detect subtle alterations that might indicate early stages or progression of AD.…”