Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: a systematic review to determine clinical applicability
Open accessAbstract
Frontotemporal dementia (FTD) is difficult to diagnose, due to its heterogeneous nature and overlap in symptoms with primary psychiatric disorders. Brain MRI for atrophy is a key biomarker but lacks sensitivity in the early stage. Morphometric MRI-based measures and machine learning techniques are a promising tool to improve diagnostic accuracy. Our aim was to review the current state of the literature using morphometric MRI to classify FTD and assess its applicability for clinical practice. A search was completed using Pubmed and PsychInfo of studies which conducted a classification of subjects with FTD from non-FTD (controls or another disorder) using morphometric MRI metrics on an individual level, using single or combined approaches. 28 relevant articles were included and systematically reviewed following PRISMA guidelines. The studies were categorized based on the type of FTD subjects included and the group(s) against which they were classified. Studies varied considerably in subject selection, MRI methodology, and classification approach, and results are highly heterogeneous. Overall many studies indicate good diagnostic accuracy, with higher performance when differentiating FTD from controls (highest result was accuracy of 100%) than other dementias (highest result was AUC of 0.874). Very few machine learning algorithms have been tested in prospective replication. In conclusion, morphometric MRI with machine learning shows potential as an early diagnostic biomarker of FTD, however studies which use rigorous methodology and validate findings in an independent real-life cohort are necessary before this method can be recommended for use clinically.
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Most studies of volumetric analytic techniques have focused on the differential diagnosis between FTD, Alzheimer's disease and controls, often yielding diagnostic accuracy in the 80-90% range.
“Most studies have focused on the differential diagnosis between FTD, Alzheimer's disease and controls, often yielding diagnostic accuracy in the 80-90% range (McCarthy et al. , 2018).”
From Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders (2020) · cites this paper as McCarthy et al. , 2018
The Results (Study characteristics) show that most reviewed studies compared FTD or bvFTD with AD or controls. The Discussion says many FTD-versus-control studies exceeded 80% or 90% accuracy, but FTD-versus-AD results were poorer; the Results also describe those results as highly variable. Thus the 80–90% characterization does not hold across the comparisons named in the claim. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMachine-learning algorithms using MRI volumetry to develop diagnostic classifiers fare well against controls but achieve maximal performance of 82% in separating well-characterized cases of bvFTD from Alzheimer's disease.
“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 McCarthy et al. , 2018
The Results sections on bvFTD versus controls and bvFTD versus AD report stronger classification against controls and 82% accuracy for a bvFTD–AD classifier. But the 82% result used cortical thickness, not MRI volumetry; the review describes volumetry as a different measure used in other studies. Table 1 also lists an 84% result for a combined-measures classifier, so 82% is not an unqualified maximum. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPrior machine learning studies had small sample sizes and training and test cohorts did not come from independent datasets, so clinical applicability remains to be determined.
“However, sample sizes were small, training and test cohorts did not come from the independent datasets and, therefore, the clinical applicability remains to be determined. 18”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [18]
The use of machine learning methods for discrimination between FTD syndromes is rarer and often only covers a few subtypes.
“However, the use of machine learning methods for discrimination between FTD syndromes is rarer (see McCarthy et al., 2018 for review), often only covering a few subtypes (Wilson et al., 2009; Bisenius et al., 2017; Di Benedetto et al., 2022).”
From Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI (2024) · cites this paper as McCarthy et al., 2018
The performance of prior machine learning models for differential diagnosis of PPA subtypes has been inconsistent, with few studies reporting high accuracy levels but with small sample sizes.
“However, the performance of these models has been inconsistent, with few studies reporting high accuracy levels but with small sample sizes (McCarthy et al., 2018).”
From Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI (2024) · cites this paper as McCarthy et al., 2018
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- Authors
- McCarthy, J., Collins, D. L., & Ducharme, S.
- Affiliations
- Montreal Neurological Institute and Hospital (CA)
- Corresponding author
- Ducharme, Simon
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 20
- Pages
- 685-696
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC6140291
- OpenAlex
- W2888949922
- Cited by
- 47
- References
- 67
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 9 · 2023: 4 · 2022: 9 · 2021: 7 · 2020: 5 · 2019: 4
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Fonds de Recherche du Québec - Santé
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Neurology › Amyotrophic Lateral Sclerosis Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- frontotemporal dementia, diagnostic biomarkers, machine learning classification, early diagnosis, brain atrophy, clinical applicability
- MeSH terms
- Machine Learning, Brain, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Biomarkers, Frontotemporal Dementia
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