ArticleEuropean radiology2017
Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using structural and advanced MRI.
Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
- Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability.NeuroImage. Clinical · 2018Pooled it
- A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.Journal of Alzheimer's disease : JAD · 2025Article
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 2025Review
- A novel neuroimaging based early detection framework for alzheimer disease using deep learning.Scientific reports · 2025Article
- Frontotemporal dementia subtyping using machine learning, multivariate statistics and neuroimaging.Brain communications · 2025Article
- Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA.Scientific reports · 2023Article
- Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia.Artificial intelligence in medicine · 2023Article
- The use of neuroimaging techniques in the early and differential diagnosis of dementia.Molecular psychiatry · 2023Review
- Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.Human brain mapping · 2023Article
- Multisystem physiological perspective of human frailty and its modulation by physical activity.Physiological reviews · 2023Review
- A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks.Life (Basel, Switzerland) · 2022Article
- Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease.Brain imaging and behavior · 2022Article
- White matter microstructure alterations in frontotemporal dementia: Phenotype-associated signatures and single-subject interpretation.Brain and behavior · 2022Article
- Concordance of regional hypoperfusion by pCASL MRI andNeuroImage. Clinical · 2022Article
- Multiparametric magnetic resonance imaging and positron emission tomography findings in neurodegenerative diseases: Current status and future directions.The neuroradiology journal · 2021Review
- An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease.Alzheimer's research & therapy · 2021Article
- Brain Volumetric Alterations in Preclinical HIV-Associated Neurocognitive Disorder Using Automatic Brain Quantification and Segmentation Tool.Frontiers in neuroscience · 2021Article
- Endothelial Progenitor Cells and Vascular Alterations in Alzheimer's Disease.Frontiers in aging neuroscience · 2021Review
- Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders.Brain : a journal of neurology · 2020Review
- Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.NeuroImage · 2020Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls.
methodsThis retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions.
resultsCombining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information.
conclusionsASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level. KEY POINTS: • Multiparametric MRI is promising for computer-aided diagnosis of early-onset AD and FTD. • Diagnosis is driven by different brain regions when using different MRI methods. • Combining structural MRI, ASL, and DTI may improve differential diagnosis of dementia.
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