Differential diagnosis of neurodegenerative diseases using structural MRI data.
Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.
VTT Technical Research Centre of Finland, Tampere, Finland; Combinostics Ltd., Tampere, Finland. Electronic address: juha.koikkalainen@combinostics.com.
Juha
J
Koikkalainen
Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
Hanneke
H
Rhodius-Meester
VTT Technical Research Centre of Finland, Tampere, Finland.
Antti
A
Tolonen
Department of Radiology and Nuclear Medicine, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
Frederik
F
Barkhof
Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
Betty
B
Tijms
Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
Afina W
AW
Lemstra
Department of Computing, Imperial College London, London, UK.
Tong
T
Tong
Department of Computing, Imperial College London, London, UK.
Ricardo
R
Guerrero
Department of Computing, Imperial College London, London, UK.
Andreas
A
Schuh
Department of Computing, Imperial College London, London, UK.
Christian
C
Ledig
Department of Computing, Imperial College London, London, UK.
Daniel
D
Rueckert
Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
Hilkka
H
Soininen
Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
Anne M
AM
Remes
Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
Gunhild
G
Waldemar
Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
Steen
S
Hasselbalch
Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy.
Patrizia
P
Mecocci
Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands; Department of Epidemiology and Biostatistics, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
Wiesje
W
van der Flier
VTT Technical Research Centre of Finland, Tampere, Finland; Combinostics Ltd., Tampere, Finland.
Jyrki
J
Lötjönen
D000368:Aged; D001931:Brain Mapping; D002544:Cerebral Infarction / Q000000981:diagnostic imaging / Q000209:etiology; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D019636:Neurodegenerative Diseases / Q000150:complications / Q000000981:diagnostic imaging*; D012189:Retrospective Studies; D066127:White Matter / Q000000981:diagnostic imaging
Alzheimer's disease; Classification; Dementia with Lewy bodies; Frontotemporal lobar degeneration; MRI; Neurodegenerative diseases; TBM; VBM; Vascular dementia; Volumetry