  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.
