ArticleDementia and geriatric cognitive disorders extra
Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis.
Article in Dementia and geriatric cognitive disorders extra. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis.Alzheimer's research & therapy · 2025Pooled it
- Diagnostic performance of the medial temporal lobe atrophy scale in patients with Alzheimer's disease: a systematic review and meta-analysis.European radiology · 2021Pooled it
- A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients.Journal of medical systems · 2019Pooled it
- Genetic overlap between cortical brain morphometry and frontotemporal dementia risk.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Article
- White matter microstructure alterations in frontotemporal dementia: Phenotype-associated signatures and single-subject interpretation.Brain and behavior · 2022Article
- State-of-the-Art Methods and Emerging Fluid Biomarkers in the Diagnostics of Dementia-A Short Review and Diagnostic Algorithm.Diagnostics (Basel, Switzerland) · 2021Review
- Detecting frontotemporal dementia syndromes using MRI biomarkers.NeuroImage. Clinical · 2019Article
- FTD spectrum: Neuroimaging across the FTD spectrum.Progress in molecular biology and translational science · 2019Review
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsWe assessed the value of automated MRI quantification methods in the differential diagnosis of behavioral-variant frontotemporal dementia (bvFTD) from Alzheimer disease (AD), Lewy body dementia (LBD), and subjective memory complaints (SMC). We also examined the role of the
methodsThe MRI scans of 50 patients with bvFTD (17
resultsOnly VBM and VOL showed utility in correctly identifying bvFTD from our set of data. The overall classification sensitivity of bvFTD with VOL + VBM achieved a total sensitivity of 60%. Using VOL + VBM, 32% were misclassified as having LBD. There was a trend of higher values for classification sensitivity of the
conclusionVOL, VBM, and their combination are effective in differential diagnostics between bvFTD and AD or SMC. However, MRI atrophy profiles for bvFTD and LBD are too similar for a reliable differentiation with the quantification methods tested in this study.
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