Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis
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| Titel: | Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis |
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| Autoren: | Veronica Ravano, Michaela Andelova, Gian Franco Piredda, Stefan Sommer, Samuele Caneschi, Lucia Roccaro, Jan Krasensky, Matej Kudrna, Tomas Uher, Ricardo A. Corredor-Jerez, Jonathan A. Disselhorst, Bénédicte Maréchal, Tom Hilbert, Jean-Philippe Thiran, Jonas Richiardi, Dana Horakova, Manuela Vaneckova, Tobias Kober |
| Weitere Verfasser: | University of Zurich, Ravano, Veronica |
| Quelle: | J Neurol Journal of neurology, vol. 271, no. 9, pp. 5944-5957 |
| Verlagsinformationen: | Springer Science and Business Media LLC, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | Male, Adult, Multiple Sclerosis, 610 Medicine & health, Lesion subtyping, Multiple Sclerosis/diagnostic imaging/pathology, Multiple sclerosis, 03 medical and health sciences, 0302 clinical medicine, Enlarging lesions, Brain/diagnostic imaging/pathology, Humans, Longitudinal Studies, Multiparametric Magnetic Resonance Imaging, Original Communication, Cross, Brain, Quantitative MRI, Middle Aged, Magnetic Resonance Imaging, Sectional Studies, 2728 Neurology (clinical), Phenotype, Cross-Sectional Studies, 2808 Neurology, Female, Multiple Sclerosis/diagnostic imaging, Multiple Sclerosis/pathology, Brain/diagnostic imaging, Brain/pathology, Disease Progression, Relaxometry, 10046 Balgrist University Hospital, Swiss Spinal Cord Injury Center |
| Beschreibung: | Background and objectives 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. Methods 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. Results and conclusions 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. |
| Publikationsart: | Article Other literature type |
| Dateibeschreibung: | application/pdf; 2024_Ravano_Microstructural_characterization_o.pdf - application/pdf |
| Sprache: | English |
| ISSN: | 1432-1459 0340-5354 |
| DOI: | 10.1007/s00415-024-12568-x |
| DOI: | 10.5167/uzh-269327 |
| Zugangs-URL: | https://pubmed.ncbi.nlm.nih.gov/39003428 http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_E7BF3FB2AF828 https://serval.unil.ch/resource/serval:BIB_E7BF3FB2AF82.P001/REF.pdf https://serval.unil.ch/notice/serval:BIB_E7BF3FB2AF82 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....c1a230060ab02e4f2da769de0242ccd3 |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Background and objectives 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. Methods 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. Results and conclusions 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. |
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| ISSN: | 14321459 03405354 |
| DOI: | 10.1007/s00415-024-12568-x |