Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness.
Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up). Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction. Using the resulting output, AD/HC classifiers were trained. Training included model tuning and performance assessment using out-of-bag estimation. Subsequently the classifiers were validated on the AD/HC test set and for the ability to predict MCI-to-AD conversion. Models' between-cohort robustness was additionally assessed using the AddNeuroMed dataset acquired with harmonized clinical and imaging protocols. In the ADNI set, the best AD/HC sensitivity/specificity (88.6%/92.0% - test set) was achieved by combining cortical thickness and volumetric measures. The Random Forest model resulted in significantly higher accuracy compared to the reference classifier (linear Support Vector Machine). The models trained using parcelled and high-dimensional (HD) input demonstrated equivalent performance, but the former was more effective in terms of computation/memory and time costs. The sensitivity/specificity for detecting MCI-to-AD conversion (but not AD/HC classification performance) was further improved from 79.5%/75%-83.3%/81.3% by a combination of morphometric measurements with ApoE-genotype and demographics (age, sex, education). When applied to the independent AddNeuroMed cohort, the best ADNI models produced equivalent performance without substantial accuracy drop, suggesting good robustness sufficient for future clinical implementation.
Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.
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AV
Lebedev
Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Alzheimer's Disease Research Centre, Karolinska Institute, Stockholm, Sweden.
E
E
Westman
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom.
G J P
GJ
Van Westen
Department of Neurology, University Medical Center Ljubljana, Slovenia.
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MG
Kramberger
Neuroinformatics and Image Analysis Laboratory, Department of Biomedicine, University of Bergen, Bergen, Norway ; Department of Radiology, Haukeland University Hospital, Bergen, Norway.
A
A
Lundervold
Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway ; Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Alzheimer's Disease Research Centre, Karolinska Institute, Stockholm, Sweden.
D
D
Aarsland
Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
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H
Soininen
Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lódz, Poland.
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I
Kłoszewska
Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy.
P
P
Mecocci
Aristotle University of Thessaloniki, Thessaloniki, Greece.
M
M
Tsolaki
GERONTOPOLE, UMR INSERM 1027, CHU, University of Toulouse, France.
B
B
Vellas
King's College London, Institute of Psychiatry, London, UK ; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia, London, UK.
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S
Lovestone
King's College London, Institute of Psychiatry, London, UK ; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia, London, UK.
A
A
Simmons
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000378:metabolism*; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards*; D008297:Male; D011237:Predictive Value of Tests
ADNI; AddNeuroMed; Alzheimer's disease; Computer-aided diagnosis; Mild cognitive impairment; Multi-center study; Random Forest; Structural MRI