Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging

TL;DR

This paper introduces a new FDG-PET based scoring system (FPDS) for assessing Alzheimer's disease progression, validated on a large dataset, showing promising accuracy for diagnosis and prediction of conversion from mild cognitive impairment.

Contribution

A novel multi-scale ensemble classification model and a 7-group stratification scheme for more accurate Alzheimer's diagnosis and progression prediction using FDG-PET imaging.

Findings

Achieved AUC of 0.78 in distinguishing DAT trajectories.

State-of-the-art AUC of 0.81 for MCI to DAT conversion prediction.

Validated on 2984 images from ADNI database.

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.