Journal
JOURNAL OF ALZHEIMERS DISEASE
Volume 47, Issue 4, Pages 939-954Publisher
IOS PRESS
DOI: 10.3233/JAD-150334
Keywords
Dementia diagnostics; machine learning; magnetic resonance imaging; prognosis; support vector machine
Categories
Funding
- Deutsche Forschungsgemeinschaft [KL 2415/2-1]
- Federal Ministry for Economic Affairs and Energy [KF3223201LW3]
- German Consortium for Frontotemporal Lobar Degeneration - German Federal Ministry of Education and Research
- Parkinson's disease Foundation [PDF-IRG-1307]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- Bio-Clinica, Inc.
- Biogen Idec Inc.
- Bristol-Myers Squibb Company
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Medpace, Inc.
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Synarc Inc.
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
- Northern California Institute for Research and Education
- NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES [TL1TR000096] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [U01AG024904, P50AG005134, P30AG013846] Funding Source: NIH RePORTER
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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.
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