Alzheimer's and vascular disease classification using regional texture biomarkers in FLAIR MRI.


Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
2023
Historique:
received: 03 12 2022
revised: 20 03 2023
accepted: 21 03 2023
medline: 19 6 2023
pubmed: 30 3 2023
entrez: 29 3 2023
Statut: ppublish

Résumé

Interactions between subcortical vascular disease and dementia due to Alzheimer's disease (AD) are unclear, and clinical overlap between the diseases makes diagnosis challenging. Existing studies have shown regional microstructural changes specific to each disease, and that textures in fluid-attenuated inversion recovery (FLAIR) MRI images may characterize abnormalities in tissue microstructure. This work aims to investigate regional FLAIR biomarkers that can differentiate dementia cohorts with and without subcortical vascular disease. FLAIR and diffusion MRI (dMRI) volumes were obtained in 65 mild cognitive impairment (MCI), 21 AD, 44 subcortical vascular MCI (scVMCI), 22 Mixed etiology, and 48 healthy elderly patients. FLAIR texture and intensity biomarkers were extracted from the normal appearing brain matter (NABM), WML penumbra, blood supply territory (BST), and white matter tract regions of each patient. All FLAIR biomarkers were correlated to dMRI metrics in each region and global WML load, and biomarker means between groups were compared using ANOVA. Binary classifications were performed using Random Forest classifiers to investigate the predictive nature of the regional biomarkers, and SHAP feature analysis was performed to further investigate optimal regions of interest for differentiating disease groups. The regional FLAIR biomarkers were strongly correlated to MD, while all biomarker regions but white matter tracts were strongly correlated to WML burden. Classification between Mixed disease and healthy, AD, and scVMCI patients yielded accuracies of 97%, 81%, and 72% respectively using WM tract biomarkers. Classification between scVMCI and healthy, MCI, and AD patients yielded accuracies of 89%, 84%, and 79% respectively using penumbra biomarkers. Only the classification between AD and healthy patients had optimal results using NABM biomarkers. This work presents novel regional FLAIR biomarkers that may quantify white matter degeneration related to subcortical vascular disease, and which indicate that investigating degeneration in specific regions may be more important than assessing global WML burden in vascular disease groups.

Identifiants

pubmed: 36989851
pii: S2213-1582(23)00074-8
doi: 10.1016/j.nicl.2023.103385
pmc: PMC10074987
pii:
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

103385

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Karissa Chan (K)

Electrical, Computer and Biomedical Engineering Department, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B 2K3, Canada; Institute for Biomedical Engineering, Science Tech (iBEST), A Partnership Between St. Michael's Hospital and Toronto Metropolitan University, 209 Victoria St., Toronto, ON M5B 1T8, Canada. Electronic address: karissa.chan@torontomu.ca.

Corinne Fischer (C)

Institute of Medical Science, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, 30 Bond St., Toronto, ON M5B 1W8, Canada; Department of Psychiatry, Faculty of Medicine, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada. Electronic address: corinne.fischer@unityhealth.to.

Pejman Jabehdar Maralani (PJ)

Department of Medical Imaging, University of Toronto, 263 McCaul St., Toronto, ON M5T 1W7, Canada. Electronic address: pejman.maralani@sunnybrook.ca.

Sandra E Black (SE)

Institute of Medical Science, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Horvitz Brain Sciences Research Program, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; L.C. Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada. Electronic address: sandra.black@sunnybrook.ca.

Alan R Moody (AR)

Department of Medical Imaging, University of Toronto, 263 McCaul St., Toronto, ON M5T 1W7, Canada. Electronic address: alan.moody@sunnybrook.ca.

April Khademi (A)

Electrical, Computer and Biomedical Engineering Department, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B 2K3, Canada; Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, 30 Bond St., Toronto, ON M5B 1W8, Canada; Institute for Biomedical Engineering, Science Tech (iBEST), A Partnership Between St. Michael's Hospital and Toronto Metropolitan University, 209 Victoria St., Toronto, ON M5B 1T8, Canada; Rotman Research Institute, Baycrest Hospital, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada. Electronic address: akhademi@torontomu.ca.

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