Ic‐p‐193: Development and Validation of a Novel Dementia of the Alzheimer's Type (Dat) Score Based on Metabolism Fdg‐pet Imaging

Abstract

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N =2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.

Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score

“…Using the stratification scheme proposed by Popuri et al (2018), we stratified the NC, MCI, and DAT groups into seven subgroups: sNC (stable NC, remained NC throughout), uNC (unstable NC, converted to MCI in the future), pNC (progressive NC, progressed to DAT in the future), sMCI (stable MCI), pMCI (progressed to DAT in the future), eDAT (converted to DAT during ADNI window), and sDAT (joined ADNI with clinical diagnosis of DAT). These subgroups represent the DAT− and DAT+ trajectories of future disease progression.…”

“…The most common approach used to extract DAT-related patterns from FDG-PET images is region-of-interest (ROI) approach (Gray et al, 2011;Lu et al, 2018;Pagani et al, 2015;Pagani et al, 2017;Popuri et al, 2018;Toussaint et al, 2012). In a ROI-based approach, a subject's FDG-PET image is registered to a corresponding structural MRI image or a custom FDG-PET template, then the mean intensity in each predefined ROI is extracted and fed into classifiers such as support vector machines.…”