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  Applying automated MR-based diagnostic methods to the memory clinic: A prospective study

Klöppel, S., Peter, J., Ludl, A., Pilatus, A., Maier, S., Mader, I., et al. (2015). Applying automated MR-based diagnostic methods to the memory clinic: A prospective study. Journal of Alzheimer's Disease, 47(4), 939-954. doi:10.3233/JAD-150334.

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Klöppel, Stefan1, 2, 3, 4, Author
Peter, Jessica2, 3, 4, Author
Ludl, Anna1, Author
Pilatus, Anne1, Author
Maier, Sabrina1, Author
Mader, Irina5, Author
Heimbach, Bernhard1, Author
Frings, Lars1, 6, Author
Egger, Karl5, Author
Dukart, Jürgen7, 8, 9, 10, Author           
Schroeter, Matthias L.8, 9, 10, Author           
Perneczky, Robert11, 12, 13, Author
Häussermann, Peter14, Author
Vach, Werner15, Author
Urbach, Horst5, Author
Teipel, Stefan16, 17, Author
Hüll, Michael1, 18, Author
Abdulkadir, Ahmed2, 19, 20, Author
1Center of Geriatrics and Gerontology, University Medical Center, Freiburg, Germany, ou_persistent22              
2Freiburg Brain Imaging, University Medical Center, Freiburg, Germany, ou_persistent22              
3Section of Gerontopsychiatry and Neuropsychology, Department of Psychiatry and Psychotherapy, University Medical Center, Freiburg, Germany, ou_persistent22              
4Department of Neurology, University Medical Center, Freiburg, Germany, ou_persistent22              
5Department of Neuroradiology, University Medical Center, Freiburg, Germany, ou_persistent22              
6Department of Nuclear Medicine, University Medical Center, Freiburg, Germany, ou_persistent22              
7Pharma Research and Early Development, F. Hoffmann-La Roche, Basel, Switzerland, ou_persistent22              
8Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              
9Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
10Consortium for Frontotemporal Lobar Degeneration, Ulm, Germany, ou_persistent22              
11Neuroepidemiology and Ageing Research Unit, School of Public Health, Imperial College London, United Kingdom, ou_persistent22              
12Cognitive Impairment and Dementia Services, Lakeside Mental Health Unit, West London Mental Health Trust, London, United Kingdom, ou_persistent22              
13Departments of Psychiatry and Psychotherapy, TU Munich, Germany, ou_persistent22              
14Departments of Gerontopsychiatry und -psychotherapy, LVR Clinic Cologne, Germany, ou_persistent22              
15Center for Medical Biometry and Medical Informatics, University Medical Center, Freiburg, Germany, ou_persistent22              
16Department of Psychosomatic Medicine, University of Rostock, Germany, ou_persistent22              
17German Center for Neurodegenerative Diseases, Rostock, Germany, ou_persistent22              
18Clinics for Geronto- and Neuropsychiatry, ZfP Emmendingen, Germany, ou_persistent22              
19Department of Computer Science, Albert Ludwigs University Freiburg, Germany, ou_persistent22              
20BIOSS Centre for Biological Signalling Studies, Albert Ludwigs University Freiburg, Germany, ou_persistent22              


Free keywords: Dementia diagnostics; Machine learning; Magnetic resonance imaging; Prognosis; Support vector machine
 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


Language(s): eng - English
 Dates: 2015-05-122015-08-112015
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3233/JAD-150334
PMID: 26401773
PMC: PMC4923764
 Degree: -



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Title: Journal of Alzheimer's Disease
  Abbreviation : J. Alzheimers Dis.
Source Genre: Journal
Publ. Info: Amsterdam : IOS Press
Pages: - Volume / Issue: 47 (4) Sequence Number: - Start / End Page: 939 - 954 Identifier: ISSN: 1387-2877
CoNE: https://pure.mpg.de/cone/journals/resource/1387-2877