Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.
Details
Serval ID
serval:BIB_E7BF3FB2AF82
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.
Journal
Journal of neurology
ISSN
1432-1459 (Electronic)
ISSN-L
0340-5354
Publication state
Published
Issued date
09/2024
Peer-reviewed
Oui
Volume
271
Number
9
Pages
5944-5957
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Abstract
In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes.
We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue.
Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.
We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue.
Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.
Keywords
Humans, Male, Female, Adult, Longitudinal Studies, Multiple Sclerosis/diagnostic imaging, Multiple Sclerosis/pathology, Phenotype, Middle Aged, Brain/diagnostic imaging, Brain/pathology, Multiparametric Magnetic Resonance Imaging, Disease Progression, Cross-Sectional Studies, Magnetic Resonance Imaging, Enlarging lesions, Lesion subtyping, Multiple sclerosis, Quantitative MRI, Relaxometry
Pubmed
Web of science
Open Access
Yes
Create date
19/07/2024 14:10
Last modification date
21/01/2025 8:29