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Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis
Möller C, Pijnenburg YA, van der Flier WM, Versteeg A, Tijms B, de Munck JC, Hafkemeijer A, Rombouts SA et al.. Radiology, 2015-12-11
Topic: Amyloid, tau and blood-test diagnosis
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.
Key findings
- 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.
- 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.
Why it matters
An image-based classifier using gray matter density maps distinguished Alzheimer's disease from behavioral variant frontotemporal dementia with accuracies of 82-88% in single-subject diagnosis. This is a diagnostic tool and does not treat, cure, or prevent Alzheimer's disease.
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Study details
- Participants115
- Evidence strengthLow
How this study scored 49/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Radiology) |
| Journal's citation record | 3 / 4 | Radiology: journal h-index 431 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2016 — within 20 year(s) of the newest study here |
| Sample size | 2 / 4 | 115 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 104 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.7 citations a year over 12 year(s) since 2016 |
| Impact for its field and year | 3 / 6 | Cited 4.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 11 other studies in this library |
| Still being cited | 1 / 4 | Cited 12 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Alle Meije Wink: h-index 43 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Erasmus MC: institution h-index 1,321 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 26653846) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 49 / 100 | — |
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Read the original
- DOI: 10.1148/radiol.2015150220
- PubMed: 26653846
- Original source →
- Free full text: Pure Amsterdam UMC →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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APA 7th
Möller, C., Pijnenburg, Y. A., van der Flier, W. M., Versteeg, A., Tijms, B., de Munck, J. C., et al. (2016). Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis. Radiology. https://doi.org/10.1148/radiol.2015150220
Vancouver
Möller C, Pijnenburg YA, van der Flier WM, Versteeg A, Tijms B, de Munck JC, et al. Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis. Radiology. 2016. doi:10.1148/radiol.2015150220. PMID: 26653846.
BibTeX
@article{moller2016-26653846,
author = {Möller, C. and Pijnenburg, Y. A. and van der Flier, W. M. and Versteeg, A. and Tijms, B. and de Munck, J. C. and Hafkemeijer, A. and Rombouts, S. A. and others},
title = {{Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis}},
journal = {Radiology},
year = {2016},
doi = {10.1148/radiol.2015150220},
pmid = {26653846},
url = {https://doi.org/10.1148/radiol.2015150220}
}
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