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

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

Research output: Contribution to journalArticleScientificpeer-review

3 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

Fingerprint

Frontotemporal Dementia
Dermatoglyphics
Alzheimer Disease
Differential Diagnosis
Area Under Curve
Computer-Assisted Decision Making
Autopsy
Biomarkers
Pathology
Aptitude
Neuropsychological Tests
Photons
Cerebrospinal Fluid
Dementia
Cohort Studies
Genotype
Tomography
Magnetic Resonance Imaging

Keywords

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

Cite this

Muñoz-Ruiz, M. Á., Hall, A., Mattila, J., Koikkalainen, J., Herukka, S-K., Husso, M., ... Hartikainen, P. (2016). Using the disease state fingerprint tool for differential diagnosis of frontotemporal dementia and alzheimer's disease. Dementia and Geriatric Cognitive Disorders Extra, 6(2), 313-329. https://doi.org/10.1159/000447122
Muñoz-Ruiz, M.Á. ; Hall, A. ; Mattila, J. ; Koikkalainen, J. ; Herukka, S.-K. ; Husso, M. ; Hänninen, T. ; Vanninen, R. ; Liu, Y. ; Hallikainen, M. ; Lötjönen, J. ; Remes, A.M. ; Alafuzoff, I. ; Soininen, H. ; Hartikainen, P. / Using the disease state fingerprint tool for differential diagnosis of frontotemporal dementia and alzheimer's disease. In: Dementia and Geriatric Cognitive Disorders Extra. 2016 ; Vol. 6, No. 2. pp. 313-329.
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title = "Using the disease state fingerprint tool for differential diagnosis of frontotemporal dementia and alzheimer's disease",
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.",
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author = "M.{\'A}. Mu{\~n}oz-Ruiz and A. Hall and J. Mattila and J. Koikkalainen and S.-K. Herukka and M. Husso and T. H{\"a}nninen and R. Vanninen and Y. Liu and M. Hallikainen and J. L{\"o}tj{\"o}nen and A.M. Remes and I. Alafuzoff and H. Soininen and P. Hartikainen",
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Muñoz-Ruiz, MÁ, Hall, A, Mattila, J, Koikkalainen, J, Herukka, S-K, Husso, M, Hänninen, T, Vanninen, R, Liu, Y, Hallikainen, M, Lötjönen, J, Remes, AM, Alafuzoff, I, Soininen, H & Hartikainen, P 2016, 'Using the disease state fingerprint tool for differential diagnosis of frontotemporal dementia and alzheimer's disease', Dementia and Geriatric Cognitive Disorders Extra, vol. 6, no. 2, pp. 313-329. https://doi.org/10.1159/000447122

Using the disease state fingerprint tool for differential diagnosis of frontotemporal dementia and alzheimer's disease. / Muñoz-Ruiz, M.Á.; Hall, A.; Mattila, J.; Koikkalainen, J.; Herukka, S.-K.; Husso, M.; Hänninen, T.; Vanninen, R.; Liu, Y.; Hallikainen, M.; Lötjönen, J.; Remes, A.M.; Alafuzoff, I.; Soininen, H.; Hartikainen, P.

In: Dementia and Geriatric Cognitive Disorders Extra, Vol. 6, No. 2, 2016, p. 313-329.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Using the disease state fingerprint tool for differential diagnosis of frontotemporal dementia and alzheimer's disease

AU - Muñoz-Ruiz, M.Á.

AU - Hall, A.

AU - Mattila, J.

AU - Koikkalainen, J.

AU - Herukka, S.-K.

AU - Husso, M.

AU - Hänninen, T.

AU - Vanninen, R.

AU - Liu, Y.

AU - Hallikainen, M.

AU - Lötjönen, J.

AU - Remes, A.M.

AU - Alafuzoff, I.

AU - Soininen, H.

AU - Hartikainen, P.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Alzheimer's disease

KW - Computer-assisted diagnosis

KW - Frontotemporal dementia

KW - Magnetic resonance imaging

KW - Neuropsychology

KW - Single-photon emission tomography

U2 - 10.1159/000447122

DO - 10.1159/000447122

M3 - Article

VL - 6

SP - 313

EP - 329

JO - Dementia and Geriatric Cognitive Disorders Extra

JF - Dementia and Geriatric Cognitive Disorders Extra

SN - 1664-5464

IS - 2

ER -