Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.
Enlarging lesions
Lesion subtyping
Multiple sclerosis
Quantitative MRI
Relaxometry
Journal
Journal of neurology
ISSN: 1432-1459
Titre abrégé: J Neurol
Pays: Germany
ID NLM: 0423161
Informations de publication
Date de publication:
13 Jul 2024
13 Jul 2024
Historique:
received:
06
02
2024
accepted:
05
07
2024
revised:
01
07
2024
medline:
14
7
2024
pubmed:
14
7
2024
entrez:
13
7
2024
Statut:
aheadofprint
Résumé
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.
Sections du résumé
BACKGROUND AND OBJECTIVES
OBJECTIVE
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
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
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.
Identifiants
pubmed: 39003428
doi: 10.1007/s00415-024-12568-x
pii: 10.1007/s00415-024-12568-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Roche
ID : NTC03706118
Organisme : Czech Ministry of Health
ID : NU 22-04-001
Organisme : Charles University Hospital Prague
ID : RVO VFN 64165
Organisme : Czech Ministry of Education
ID : Project cooperation LF1
Informations de copyright
© 2024. The Author(s).
Références
Granziera C, Wuerfel J, Barkhof F et al (2021) Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain 144:1296–1311. https://doi.org/10.1093/brain/awab029
doi: 10.1093/brain/awab029
pubmed: 33970206
pmcid: 8219362
Vaneckova M, Piredda GF, Andelova M et al (2022) Periventricular gradient of T1 tissue alterations in multiple sclerosis. Neuroimage Clin 34:1–10. https://doi.org/10.1016/j.nicl.2022.103009
doi: 10.1016/j.nicl.2022.103009
Chen X, Schädelin S, Lu P-J et al (2023) Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients. Neuroimage Clin. https://doi.org/10.1016/j.nicl.2023.103349
doi: 10.1016/j.nicl.2023.103349
pubmed: 38150745
pmcid: 10777105
Barkhof F (2002) The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 15:239–245. https://doi.org/10.1097/00019052-200206000-00003
doi: 10.1097/00019052-200206000-00003
pubmed: 12045719
Weber CE, Wittayer M, Kraemer M et al (2021) Quantitative MRI texture analysis in chronic active multiple sclerosis lesions. Magn Reson Imaging 79:97–102. https://doi.org/10.1016/j.mri.2021.03.016
doi: 10.1016/j.mri.2021.03.016
pubmed: 33771609
Harrison DM, Li X, Liu H et al (2016) Lesion heterogeneity on high-field susceptibility MRI Is associated with multiple sclerosis severity. Am J Neuroradiol 37:1447–1453. https://doi.org/10.3174/ajnr.A4726
doi: 10.3174/ajnr.A4726
pubmed: 26939635
pmcid: 4983536
Calvi A, Haider L, Prados F et al (2022) In vivo imaging of chronic active lesions in multiple sclerosis. Mult Scler J 28:683–690. https://doi.org/10.1177/1352458520958589
doi: 10.1177/1352458520958589
Calvi A, Carrasco FP, Tur C et al (2022) Association of slowly expanding lesions on MRI with disability in people with secondary progressive multiple sclerosis. Neurology 98:E1783–E1793. https://doi.org/10.1212/WNL.0000000000200144
doi: 10.1212/WNL.0000000000200144
pubmed: 35277438
Absinta M, Sati P, Masuzzo F et al (2019) Association of chronic active multiple sclerosis lesions with disability in vivo. JAMA Neurol 76:1474–1483. https://doi.org/10.1001/jamaneurol.2019.2399
doi: 10.1001/jamaneurol.2019.2399
pubmed: 31403674
pmcid: 6692692
Preziosa P, Pagani E, Meani A et al (2022) Slowly expanding lesions predict 9-year multiple sclerosis disease progression. Neurology(R) Neuroimmunol Neuroinflamm 9:1–11. https://doi.org/10.1212/NXI.0000000000001139
doi: 10.1212/NXI.0000000000001139
Elliott C, Wolinsky JS, Hauser SL et al (2019) Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions. Multiple Sclerosis J 25:1915–1925. https://doi.org/10.1177/1352458518814117
doi: 10.1177/1352458518814117
Rahmanzadeh R, Galbusera R, Lu PJ, Bahn E, Weigel M, Barakovic M, Franz J, Nguyen TD, Spincemaille P, Schiavi S, Daducci A, La Rosa F, Absinta M, Sati P, Bach Cuadra M, Radue EW, Leppert D, Kuhle J, Kappos L, Brück W, Reich DS, Stadelmann C, Wang Y, Granziera C (2022) A New Advanced MRI Biomarker for Remyelinated Lesions in Multiple Sclerosis. Ann Neurol 92(3):486–502. https://doi.org/10.1002/ana.26441 . PMID: 35713309; PMCID: PMC9527017
Shi Z, Pan Y, Yan Z et al (2023) Microstructural alterations in different types of lesions and their perilesional white matter in relapsing-remitting multiple sclerosis based on diffusion kurtosis imaging. Mult Scler Relat Disord 71:104572. https://doi.org/10.1016/j.msard.2023.104572
doi: 10.1016/j.msard.2023.104572
pubmed: 36821979
Wittayer M, Weber CE, Krämer J et al (2023) Exploring (peri-) lesional and structural connectivity tissue damage through T1/T2-weighted ratio in iron rim multiple sclerosis lesions. Magn Reson Imaging 95:12–18. https://doi.org/10.1016/j.mri.2022.10.009
doi: 10.1016/j.mri.2022.10.009
pubmed: 36270415
Calvi A, Clarke MA, Prados F et al (2023) Relationship between paramagnetic rim lesions and slowly expanding lesions in multiple sclerosis. Mult Scler 29:352–362. https://doi.org/10.1177/13524585221141964
doi: 10.1177/13524585221141964
pubmed: 36515487
Elliott C, Rudko DA, Arnold DL et al (2023) Lesion-level correspondence and longitudinal properties of paramagnetic rim and slowly expanding lesions in multiple sclerosis. Multi Sclerosis J. https://doi.org/10.1177/13524585231162262
doi: 10.1177/13524585231162262
Marques JP, Kober T, Krueger G et al (2010) MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49:1271–1281. https://doi.org/10.1016/j.neuroimage.2009.10.002
doi: 10.1016/j.neuroimage.2009.10.002
pubmed: 19819338
Mussard E, Hilbert T, Forman C et al (2020) Accelerated MP2RAGE imaging using Cartesian phyllotaxis readout and compressed sensing reconstruction. Magn Reson Med 84:1881–1894. https://doi.org/10.1002/mrm.28244
doi: 10.1002/mrm.28244
pubmed: 32176826
Hilbert T, Sumpf TJ, Weiland E et al (2018) Accelerated T 2 mapping combining parallel MRI and model-based reconstruction: GRAPPATINI. J Magn Reson Imaging 48:359–368. https://doi.org/10.1002/jmri.25972
doi: 10.1002/jmri.25972
pubmed: 29446508
Tsang A, Wager C, Corredor-Jerez R et al (2018) Comparison of techniques for measurement of brain volume in multiple sclerosis patients. Neurology (Conference abstract) 90(P3):354
Fartaria MJ, Todea A, Kober T et al (2018) Partial volume-aware assessment of multiple sclerosis lesions. Neuroimage Clin 18:245–253. https://doi.org/10.1016/j.nicl.2018.01.011
doi: 10.1016/j.nicl.2018.01.011
pubmed: 29868448
pmcid: 5984601
Fartaria MJ, Bonnier G, Roche A et al (2016) Automated detection of white matter and cortical lesions in early stages of multiple sclerosis. J Magn Reson Imaging 43:1445–1454. https://doi.org/10.1002/jmri.25095
doi: 10.1002/jmri.25095
pubmed: 26606758
Klein S, Staring M, Murphy K et al (2010) elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29:196–205. https://doi.org/10.1109/TMI.2009.2035616
doi: 10.1109/TMI.2009.2035616
pubmed: 19923044
Schmitter D, Roche A, Maréchal B et al (2015) An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease. Neuroimage Clin 7:7–17. https://doi.org/10.1016/j.nicl.2014.11.001
doi: 10.1016/j.nicl.2014.11.001
pubmed: 25429357
Piredda GF, Hilbert T, Granziera C et al (2020) Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology. Magn Reson Med 83:337–351. https://doi.org/10.1002/mrm.27927
doi: 10.1002/mrm.27927
pubmed: 31418910
Elkin LA, Kay M, Higgins JJ, Wobbrock JO (2021) An aligned rank transform procedure for multifactor contrast tests. In: UIST 2021—Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology, pp 754–768. https://doi.org/10.1145/3472749.3474784
Liaw A, Wiener M (2002) The R Journal: classification and regression by randomForest. R Journal 2:18–22
Greenwell BM, Boehmke BC (2020) Variable importance plots—an introduction to the vip package. R Journal 12:343–366. https://doi.org/10.32614/rj-2020-013
doi: 10.32614/rj-2020-013
Fox J, Weisberg S (2019) An R companion to applied regression. Sage
Boaventura M, Sastre-Garriga J, Garcia-Vidal A et al (2022) T1/T2-weighted ratio in multiple sclerosis: a longitudinal study with clinical associations. Neuroimage Clin. https://doi.org/10.1016/j.nicl.2022.102967
doi: 10.1016/j.nicl.2022.102967
pubmed: 35202997
pmcid: 8866895
Uddin MN, Figley TD, Marrie RA, Figley CR (2018) Can T1w/T2w ratio be used as a myelin-specific measure in subcortical structures? Comparisons between FSE-based T1w/T2w ratios, GRASE-based T1w/T2w ratios and multi-echo GRASE-based myelin water fractions. NMR Biomed 31:e3868. https://doi.org/10.1002/nbm.3868
doi: 10.1002/nbm.3868
Elliott C, Arnold DL, Chen H et al (2020) Patterning chronic active demyelination in slowly expanding/evolving white matter MS lesions. Am J Neuroradiol 41:1–8. https://doi.org/10.3174/ajnr.A6742
doi: 10.3174/ajnr.A6742
Moog TM, McCreary M, Wilson A et al (2022) Direction and magnitude of displacement differ between slowly expanding and non-expanding multiple sclerosis lesions as compared to small vessel disease. J Neurol 269:4459–4468. https://doi.org/10.1007/s00415-022-11089-9
doi: 10.1007/s00415-022-11089-9
pubmed: 35380254
Dal-Bianco A, Grabner G, Kronnerwetter C et al (2017) Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol 133:25–42. https://doi.org/10.1007/s00401-016-1636-z
doi: 10.1007/s00401-016-1636-z
pubmed: 27796537
Frischer JM, Weigand SD, Guo Y et al (2015) Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol 78:710–721. https://doi.org/10.1002/ana.24497
doi: 10.1002/ana.24497
pubmed: 26239536
pmcid: 4623970
Dwyer MG, Bergsland N, Ramasamy DP et al (2018) Atrophied brain lesion volume: a new imaging biomarker in multiple sclerosis. J Neuroimaging 28:490–495. https://doi.org/10.1111/jon.12527
doi: 10.1111/jon.12527
pubmed: 29856910
Zivadinov R, Bergsland N, Dwyer MG (2018) Atrophied brain lesion volume, a magnetic resonance imaging biomarker for monitoring neurodegenerative changes in multiple sclerosis. Quant Imaging Med Surg 8:979–983. https://doi.org/10.21037/qims.2018.11.01
doi: 10.21037/qims.2018.11.01
pubmed: 30598875
pmcid: 6288055
Levesque I, Sled JG, Narayanan S et al (2005) The role of edema and demyelination in chronic T1 black holes: a quantitative magnetization transfer study. J Magn Reson Imaging 21:103–110. https://doi.org/10.1002/jmri.20231
doi: 10.1002/jmri.20231
pubmed: 15666408
Van Walderveen MAA, Kamphorst W, Scheltens P et al (1998) Histopathologic correlate of hypointense lesions on T1-weighted spin-echo MRI in multiple sclerosis. Neurology 50:1282–1288. https://doi.org/10.1212/WNL.50.5.1282
doi: 10.1212/WNL.50.5.1282
pubmed: 9595975
Fisher E, Chang A, Fox RJ et al (2007) Imaging correlates of axonal swelling in chronic multiple sclerosis brains. Ann Neurol 62:219–228. https://doi.org/10.1002/ana.21113
doi: 10.1002/ana.21113
pubmed: 17427920
Trapp BD, Vignos M, Dudman J et al (2018) Cortical neuronal densities and cerebral white matter demyelination in multiple sclerosis: a retrospective study. Lancet Neurol 17:870–884. https://doi.org/10.1016/S1474-4422(18)30245-X
doi: 10.1016/S1474-4422(18)30245-X
pubmed: 30143361
pmcid: 6197820