Using the disease state fingerprint tool for differential diagnosis of frontotemporal dementia and alzheimer's disease

M.Á. Muñoz-Ruiz, A. Hall, J. Mattila, Juha Koikkalainen, S.-K. Herukka, M. Husso, T. Hänninen, R. Vanninen, Y. Liu, M. Hallikainen, Jyrki Lötjönen, A.M. Remes, I. Alafuzoff, H. Soininen, P. Hartikainen

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)313-329
JournalDementia and Geriatric Cognitive Disorders Extra
Volume6
Issue number2
DOIs
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Alzheimer's disease
  • Computer-assisted diagnosis
  • Frontotemporal dementia
  • Magnetic resonance imaging
  • Neuropsychology
  • Single-photon emission tomography

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