Alzheimer's disease and frontotemporal dementia are differentiated by discriminant analysis applied to 99mTc HmPAO SPECT data

Objective-Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the most frequent neurodegenerative cognitive disorders. However, FTD remains poorly recognised clinically. The use of (99m)HmPAO-single photon emission computed tomography (SPECT) has been demonstrated in the differentiation of AD and FTD. Nethertheless, there are very few comparative studies designed to assess its precise value in this differential diagnosis. The aim was to determine a simple decision rule, deduced from statistical analysis, which, if applied to regions of interest (ROIs) and mini mental state examination (MMSE), could improve the predictive value of SPECT in differential diagnosis between AD and FTD. Methods-Forty patients, 20 with probable AD and 20 with probable FTD were included. All patients underwent brain SPECT imaging, after an intravenous injection of Tc-99m HmPAO-(555mBq). For each patient, 20 ROIs were determined on the Fleishig's slice and their activity was normalised to the mean cerebellar activity. Bivariate analysis (Wilcoxon rank tests) and multivariate analysis (stepwise discriminant analysis) were performed to determine the subgroup of variables able to give the highest predictive value for this differential diagnosis. A simple decision rule was built from a predictive score derived by factorial discriminant analysis. Results-As previously described, the fixation defect was found in frontal regions of interest (ROIs) in FTD and in the left temporoparietal-occipital ROIs in AD. Among the 21 variables, five were finally selected: right median frontal, left lateral frontal, left tempoparietal, left temporoparietal-occipital areas, and MMSE. One hundred per cent of patients with FTD were correctly classified by the decision rule (20/20 patients) and 90% of patients with AD (18/20). Conclusion-AD and FTD are differentiated by SPECT. Automatic classification based on a decision rule deduced from factorial discriminant analysis could enhance its performance.