In Journal of neurology
BACKGROUND : To differentiate dementia with Lewy bodies (DLB) from Alzheimer disease (AD) using a single imaging modality is challenging, because of their common hypometabolic findings. Scaled subprofile modeling/principal component analysis (SSM/PCA), an unsupervised artificial intelligence, has the potential to offer an alternative to image analysis.
OBJECTIVE : We aimed to produce spatial metabolic profiles to discriminate DLB from AD and to identify the characteristics of the profiles.
METHODS : Fifty individuals each with DLB, AD, and normal cognition (NL) underwent 18F-FDG-PET and MRI. The spatial metabolic profile to differentiate DLB from AD (DLB-AD discrimination profile) was determined using SSM/PCA with tenfold cross validation. For comparison, we also produced disease-related profiles that can discriminate AD and DLB from NL (AD- and DLB-related profiles, respectively).
RESULTS : The DLB-AD discrimination profile significantly differentiated DLB from AD with comparable accuracy to that of discriminating DLB and AD from NL. The AD- and DLB-related profiles comprised metabolic imaging features typical of each pathology. In contrast, the DLB-AD discrimination profile emphasized preservation in the posterior cingulate cortex (cingulate island sign) and medial temporal lobe, and occipital hypometabolism. Common hypometabolic findings between DLB and AD were less noticeable in the profile. The DLB-related profile significantly correlated with cognitive function and three core features of DLB, whereas the DLB-AD discrimination profile did not.
CONCLUSIONS : Spatial metabolic profile that could discriminate DLB from AD emphasized different imaging features and eliminated common findings between DLB and AD. Neither cognitive function nor core features were associated with the profile.
Iizuka Tomomichi, Kameyama Masashi
Alzheimer disease, Artificial intelligence, Cingulate island sign, Dementia with Lewy bodies, FDG-PET, Spatial metabolic profile