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Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis
Open accessAbstract
Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.
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“Recently, machine learning techniques have been applied to distinguish between bvFTD and Cognitively Normal Subjects (CNCs), Alzheimer Disease or other psychiatric and neurologic disorders on an individual level using MRI-based features. 9-17”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [12]
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“In general, they achieved moderate to high accuracy distinguishing bvFTD.”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [12]
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“Previous studies classified bvFTD from a control group. 9-13”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [12]
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“Machine-learning algorithms using MRI volumetry to develop diagnostic classifiers fare well against controls but achieve maximal performance of 82% in separating wellcharacterized cases of bvFTD from Alzheimer's disease (Moller et al. , 2016; Canu et al. , 2017; McCarthy et al. , 2018).”
From Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders (2020) · cites this paper as Moller et al. , 2016
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Metadata
- Authors
- Möller, C., Pijnenburg, Y. A. L., van der Flier, W. M., Versteeg, A., Tijms, B., de Munck, J. C., Hafkemeijer, A., Rombouts, S. A. R. B., van der Grond, J., van Swieten, J., Dopper, E., Scheltens, P., Barkhof, F., Vrenken, H., & Wink, A. M.
- Affiliations
- Erasmus MC (NL)
- Leiden University (NL)
- ORCID
- Venue
- Radiology
- Published
- 2016-06
- Volume
- 279
- Issue
- 3
- Pages
- 838-848
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2192985207
- Cited by
- 103
- References
- 47
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 15 · 2022: 22 · 2021: 14 · 2020: 8 · 2019: 12 · 2018: 15 · 2017: 2 · 2016: 3
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- green
- pure.amsterdamumc.nl
- Licence
- other-oa
- Locations
- Radiology (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Funders
- Vanderbilt University Medical Center
- Vrije Universiteit Amsterdam
- Stichting Dioraphte
- Alzheimer Nederland
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, behavioral variant frontotemporal dementia, cortical atrophy, support vector machine, T1-weighted structural MRI, differential diagnosis
- MeSH terms
- Alzheimer Disease, Atrophy, Diagnosis, Differential, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, ROC Curve, Frontotemporal Dementia, Support Vector Machine, Gray Matter
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