Background: Disease State Index (DSI) and its visualization, Disease State Fingerprint (DSF), form a computer-assisted clinical decision making tool that combines patient data and compares them with cases with known outcomes.
Aims: To investigate the ability of the DSI to diagnose frontotemporal dementia (FTD) and Alzheimer's disease (AD).
Methods: The study cohort consisted of 38 patients with FTD, 57 with AD and 22 controls. Autopsy verification of FTD with TDP-43 positive pathology was available for 14 and AD pathology for 12 cases. We utilized data from neuropsychological tests, volumetric magnetic resonance imaging, single-photon emission tomography, cerebrospinal fluid biomarkers and the APOE genotype. The DSI classification results were calculated with a combination of leave-one-out cross-validation and bootstrapping. A DSF visualization of a FTD patient is presented as an example.
Results: The DSI distinguishes controls from FTD (area under the receiver-operator curve, AUC = 0.99) and AD (AUC = 1.00) very well and achieves a good differential diagnosis between AD and FTD (AUC = 0.89). In subsamples of autopsy-confirmed cases (AUC = 0.97) and clinically diagnosed cases (AUC = 0.94), differential diagnosis of AD and FTD performs very well.
Conclusions: DSI is a promising computer-assisted biomarker approach for aiding in the diagnostic process of dementing diseases. Here, DSI separates controls from dementia and differentiates between AD and FTD.
Keywords: Alzheimer's disease; Computer-assisted diagnosis; Frontotemporal dementia; Magnetic resonance imaging; Neuropsychology; Single-photon emission tomography.