SynthesisNeuroImage. Clinical2018
Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability.
Synthesis in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
22 citing papers in PubMed, 50 citations in OpenAlex.
- Towards a Multidimensional Model of Neurocognitive Disorders (MOND Model): Integrating Evidence from a Critical Review into a Model for Future Research.Journal of personalized medicine · 2026Review
- Morphometric features enhance phenotype discrimination in frontotemporal lobar degeneration.Brain communications · 2026Article
- Current Status and Future Aspects of Gadolinium Oxide Nanoparticles as Positive Magnetic Resonance Imaging Contrast Agents.Nanomaterials (Basel, Switzerland) · 2025Review
- A Self-supervised Deep Learning Model for Diagonal Sulcus Detection with Limited Labeled Data.Neuroinformatics · 2025Article
- Frontotemporal dementia subtyping using machine learning, multivariate statistics and neuroimaging.Brain communications · 2025Article
- Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.International journal of computer assisted radiology and surgery · 2024Article
- VentRa: distinguishing frontotemporal dementia from psychiatric disorders.Brain communications · 2024Article
- Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.Frontiers in neuroscience · 2024Article
- Investigation of the functional pathogenesis of mild cognitive impairment by localisation-based locus coeruleus resting-state fMRI.Human brain mapping · 2022Article
- A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks.Life (Basel, Switzerland) · 2022Article
- Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study.NPJ digital medicine · 2022Article
- Ventricular features as reliable differentiators between bvFTD and other dementias.NeuroImage. Clinical · 2022Article
- The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia.Frontiers in neurology · 2022Review
- Quantified Brain Magnetic Resonance Imaging Volumes Differentiate Behavioral Variant Frontotemporal Dementia from Early-Onset Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI.Frontiers in neuroscience · 2022Article
- MNI-FTD templates, unbiased average templates of frontotemporal dementia variants.Scientific data · 2021Article
- Diagnostic Utility of Measuring Cerebral Atrophy in the Behavioral Variant of Frontotemporal Dementia and Association With Clinical Deterioration.JAMA network open · 2021Article
- Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders.Brain : a journal of neurology · 2020Review
- CCCDTD5: Clinical role of neuroimaging and liquid biomarkers in patients with cognitive impairment.Alzheimer's & dementia (New York, N. Y.) · 2020Review
- Diagnostic imaging of dementia with Lewy bodies, frontotemporal lobar degeneration, and normal pressure hydrocephalus.Japanese journal of radiology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.