Journal article
Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study
Journal of Alzheimer's disease, Vol.47(4), pp.939-954
2015
DOI: 10.3233/JAD-150334
PMCID: PMC4923764
PMID: 26401773
Abstract
Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer's disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC > 0.9) but substantially less accurate (AUC = 0.76 for AD, AUC = 0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies.
Details
- Title: Subtitle
- Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study
- Creators
- Stefan Klöppel - Department of Neurology, University Medical Center Freiburg, Freiburg, GermanyJessica Peter - Department of Neurology, University Medical Center Freiburg, Freiburg, GermanyAnna Ludl - Center of Geriatrics and Gerontology Freiburg, University Medical Center Freiburg, Freiburg, GermanyAnne Pilatus - Center of Geriatrics and Gerontology Freiburg, University Medical Center Freiburg, Freiburg, GermanySabrina Maier - Center of Geriatrics and Gerontology Freiburg, University Medical Center Freiburg, Freiburg, GermanyIrina Mader - Department of Neuroradiology, University Medical Center Freiburg, Freiburg, GermanyBernhard Heimbach - Center of Geriatrics and Gerontology Freiburg, University Medical Center Freiburg, Freiburg, GermanyLars Frings - Department of Nuclear Medicine, University Medical Center Freiburg, Freiburg, GermanyKarl Egger - Department of Neuroradiology, University Medical Center Freiburg, Freiburg, GermanyJuergen Dukart - Max Planck Institute for Human Cognitive and Brain Sciences & Clinic for Cognitive Neurology, University of Leipzig, and German Consortium for Frontotemporal Lobar Degeneration, Ulm, GermanyMatthias L Schroeter - Max Planck Institute for Human Cognitive and Brain Sciences & Clinic for Cognitive Neurology, University of Leipzig, and German Consortium for Frontotemporal Lobar Degeneration, Ulm, GermanyRobert Perneczky - Departments of Psychiatry and Psychotherapy, Technical University München, GermanyPeter HäussermannWerner Vach - Center for Medical Biometry and Medical Informatics, University of Freiburg, GermanyHorst Urbach - Department of Neuroradiology, University Medical Center Freiburg, Freiburg, GermanyStefan Teipel - Departments of Psychosomatic Medicine, University of Rostock, and German Center for Neurodegenerative Diseases (DZNE), Rostock, GermanyMichael Hüll - Clinics for Geronto- and Neuropsychiatry, ZfP Emmendingen, Emmendingen, GermanyAhmed Abdulkadir - Department of Computer Science and BIOSS Centre for Biological Signaling Studies, University of Freiburg, GermanyAlzheimer’s Disease Neuroimaging Initiative
- Contributors
- Laura L Boles-Ponto (Contributor) - University of Iowa, Radiology
- Resource Type
- Journal article
- Publication Details
- Journal of Alzheimer's disease, Vol.47(4), pp.939-954
- Publisher
- Netherlands
- DOI
- 10.3233/JAD-150334
- PMID
- 26401773
- PMCID
- PMC4923764
- ISSN
- 1387-2877
- eISSN
- 1875-8908
- Grant note
- P30 AG062421 / NIA NIH HHS P50 AG005134 / NIA NIH HHS P30 AG013846 / NIA NIH HHS U01 AG024904 / NIA NIH HHS
- Language
- English
- Date published
- 2015
- Academic Unit
- Radiology; Pharmaceutical Sciences and Experimental Therapeutics
- Record Identifier
- 9984051579202771
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