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Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI
OBJECTIVES: To 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. METHODS: This retrospective study used MRI data...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5491625/ https://www.ncbi.nlm.nih.gov/pubmed/27986990 http://dx.doi.org/10.1007/s00330-016-4691-x |
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author | Bron, Esther E. Smits, Marion Papma, Janne M. Steketee, Rebecca M. E. Meijboom, Rozanna de Groot, Marius van Swieten, John C. Niessen, Wiro J. Klein, Stefan |
author_facet | Bron, Esther E. Smits, Marion Papma, Janne M. Steketee, Rebecca M. E. Meijboom, Rozanna de Groot, Marius van Swieten, John C. Niessen, Wiro J. Klein, Stefan |
author_sort | Bron, Esther E. |
collection | PubMed |
description | OBJECTIVES: To 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. METHODS: This 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. RESULTS: Combining 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. CONCLUSIONS: ASL 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00330-016-4691-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5491625 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-54916252017-07-13 Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI Bron, Esther E. Smits, Marion Papma, Janne M. Steketee, Rebecca M. E. Meijboom, Rozanna de Groot, Marius van Swieten, John C. Niessen, Wiro J. Klein, Stefan Eur Radiol Computer Applications OBJECTIVES: To 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. METHODS: This 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. RESULTS: Combining 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. CONCLUSIONS: ASL 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00330-016-4691-x) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2016-12-16 2017 /pmc/articles/PMC5491625/ /pubmed/27986990 http://dx.doi.org/10.1007/s00330-016-4691-x Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Computer Applications Bron, Esther E. Smits, Marion Papma, Janne M. Steketee, Rebecca M. E. Meijboom, Rozanna de Groot, Marius van Swieten, John C. Niessen, Wiro J. Klein, Stefan Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI |
title | Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI |
title_full | Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI |
title_fullStr | Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI |
title_full_unstemmed | Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI |
title_short | Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI |
title_sort | multiparametric computer-aided differential diagnosis of alzheimer’s disease and frontotemporal dementia using structural and advanced mri |
topic | Computer Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5491625/ https://www.ncbi.nlm.nih.gov/pubmed/27986990 http://dx.doi.org/10.1007/s00330-016-4691-x |
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