Cited by 103Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsArtificial Intelligence in Healthcare

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Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis

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Möller, Christiane, Pijnenburg, Yolande A. L., van der Flier, Wiesje M., Versteeg, Adriaan, Tijms, Betty, de Munck, Jan C., Hafkemeijer, Anne, Rombouts, Serge A. R. B., van der Grond, Jeroen, van Swieten, John, Dopper, Elise, Scheltens, Philip, Barkhof, Frederik, Vrenken, Hugo, Wink, Alle Meije·2016·Radiology

DOI 10.1148/radiol.2015150220

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Abstract

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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Additional file 2 of Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records
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Additional file 4 of Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records
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Additional file 1 of An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease
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Additional file 3 of Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records
Vyas, A., Aisopos, F., Vidal, M.-E., Garrard, P., & Παλιούρας, Γ. (2022) · Figshare · doi:10.6084/m9.figshare.21351437
Additional file 2 of An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease
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Additional file 1 of Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records
Vyas, A., Aisopos, F., Vidal, M.-E., Garrard, P., & Παλιούρας, Γ. (2022) · Figshare · doi:10.6084/m9.figshare.21351431
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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: Möller, Christiane
Pijnenburg, Yolande A. L.
van der Flier, Wiesje M.
Versteeg, Adriaan
Tijms, Betty
de Munck, Jan C.
Hafkemeijer, Anne
Rombouts, Serge A. R. B.
van der Grond, Jeroen
Dopper, Elise
Scheltens, Philip
Barkhof, Frederik
Vrenken, Hugo
Wink, Alle Meije
Venue: Radiology
Publisher: Radiological Society of North America (RSNA)
Published: 2016-06
Volume: 279
Issue: 3
Pages: 838-848
ISSN: 0033-8419 (ISSN-L)
1527-1315
DOI: 10.1148/radiol.2015150220
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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Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis

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