Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.
Clinically isolated syndrome
Machine learning
Magnetic resonance imaging
Multiple sclerosis
Prognosis
Support vector machine
Journal
Neuroradiology
ISSN: 1432-1920
Titre abrégé: Neuroradiology
Pays: Germany
ID NLM: 1302751
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
27
08
2021
accepted:
15
12
2021
pubmed:
21
1
2022
medline:
11
6
2022
entrez:
20
1
2022
Statut:
ppublish
Résumé
To predict the occurrence of a second clinical event in patients with a CIS suggestive of MS, from baseline magnetic resonance imaging (MRI), by means of a pattern recognition approach. Two hundred sixty-six patients with a CIS were recruited from four participating centers. Over a follow-up of 3 years, 130 patients had a second clinical episode and 136 did not. Grey matter and white matter T1-hypointensities masks segmented from 3D T1-weighted images acquired on 3 T scanners were used as features for the classification approach. Differences between CIS that remained CIS and those that developed a second event were assessed at a global level and at a regional level, arranging the regions according to their contribution to the classification model. All classification metrics were around or even below 50% for both global and regional approaches. Accuracies did not change when T1-hypointensity maps were added to the model; just the specificity was increased up to 80%. Among the 30 regions with the largest contribution, 26 were grey matter and 4 were white matter regions. For grey matter, regions contributing showed either a larger or a smaller volume in the group of patients that remained CIS, compared to those with a second event. The volume of T1-hypointensities was always larger for the group that presented a second event. Prediction of a second clinical event in CIS patients from baseline MRI seems to present a highly heterogeneous pattern, leading to very low classification accuracies. Adding the T1-hypointensity maps does not seem to improve the accuracy of the classification model.
Identifiants
pubmed: 35048162
doi: 10.1007/s00234-021-02885-7
pii: 10.1007/s00234-021-02885-7
doi:
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1383-1390Subventions
Organisme : Instituto de Salud Carlos III
ID : PI18/00823
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Miller DH, Chard DT, Ciccarelli O (2012) Clinically isolated syndromes. Lancet Neurol 11(2):157–169
doi: 10.1016/S1474-4422(11)70274-5
Lassmann H (2008) The pathologic substrate of magnetic resonance alterations in multiple sclerosis. Neuroimaging Clin N Am 18(4):563–576
doi: 10.1016/j.nic.2008.06.005
Filippi M, Preziosa P, Banwell BL et al (2019) Assessment of lesions on magnetic resonance imaging in multiple sclerosis: practical guidelines. Brain 142(7):1858–1875
doi: 10.1093/brain/awz144
Sastre-Garriga J, Pareto D, Battaglini M et al (2020) MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat Rev Neurol 16(3):171–182
doi: 10.1038/s41582-020-0314-x
Kuhle J, Disanto G, Dobson R et al (2015) Conversion from clinically isolated syndrome to multiple sclerosis: a large multicentre study. Mult Scler 21(8):1013–1024
doi: 10.1177/1352458514568827
Tintoré M, Rovira A, Río J et al (2006) Baseline MRI predicts future attacks and disability in clinically isolated syndromes. Neurology 67(6):968–972
doi: 10.1212/01.wnl.0000237354.10144.ec
Zhang H, Alberts E, Pongratz V et al (2018) Predicting conversion from clinically isolated syndrome to multiple sclerosis-an imaging-based machine learning approach. Neuroimage Clin S2213–1582(18):30341–30343
Wottschel V, Alexander DC, Kwok PP et al (2014) Predicting outcome in clinically isolated syndrome using machine learning. Neuroimage Clin 7:281–287
doi: 10.1016/j.nicl.2014.11.021
Wottschel V, Chard DT, Enzinger C et al (2019) SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis. Neuroimage Clin. 24:102011
doi: 10.1016/j.nicl.2019.102011
Bendfeldt K, Taschler B, Gaetano L et al (2019) MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry. Brain Imaging Behav 13(5):1361–2137
doi: 10.1007/s11682-018-9942-9
Schrouff J, Rosa MJ, Rondina JM et al (2013) PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics 11(3):319–337
doi: 10.1007/s12021-013-9178-1
Schrouff J, Monteiro JM, Portugal L et al (2018) Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models. Neuroinformatics 16(1):117–143
doi: 10.1007/s12021-017-9347-8
Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1):273–289
doi: 10.1006/nimg.2001.0978
Mori S, Oishi K, Jiang H et al (2008) Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40(2):570–582
doi: 10.1016/j.neuroimage.2007.12.035
Henry RG, Shieh M, Okuda DT et al (2008) Regional grey matter atrophy in clinically isolated syndromes at presentation. J Neurol Neurosurg Psychiatry 79(11):1236–1244
doi: 10.1136/jnnp.2007.134825
Pérez-Miralles F, Sastre-Garriga J, Tintoré M et al (2013) Clinical impact of early brain atrophy in clinically isolated syndromes. Mult Scler 19(14):1878–1886
doi: 10.1177/1352458513488231
Audoin B, Zaaraoui W, Reuter F et al (2010) Atrophy mainly affects the limbic system and the deep grey matter at the first stage of multiple sclerosis. J Neurol Neurosurg Psychiatry 81(6):690–695
doi: 10.1136/jnnp.2009.188748
Pareto D, Sastre-Garriga J, Alberich M et al (2019) Brain regional volume estimations with NeuroQuant and FIRST: a study in patients with a clinically isolated syndrome. Neuroradiology 61(6):667–767
doi: 10.1007/s00234-019-02191-3