MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry

Kerstin Bendfeldt, Bernd Taschler, Laura Gaetano, Philip Madoerin, Pascal Kuster, Nicole Mueller-Lenke, Michael Amann, Hugo Vrenken, Viktor Wottschel, Frederik Barkhof, Stefan Borgwardt, Stefan Klöppel, Eva Maria Wicklein, Ludwig Kappos, Gilles Edan, Mark S. Freedman, Xavier Montalbán, Hans Peter Hartung, Christoph Pohl, Rupert SandbrinkTill Sprenger, Ernst Wilhelm Radue, Jens Wuerfel, Thomas E. Nichols

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Neuroanatomical pattern classification using support vector machines (SVMs) has shown promising results in classifying Multiple Sclerosis (MS) patients based on individual structural magnetic resonance images (MRI). To determine whether pattern classification using SVMs facilitates predicting conversion to clinically definite multiple sclerosis (CDMS) from clinically isolated syndrome (CIS). We used baseline MRI data from 364 patients with CIS, randomised to interferon beta-1b or placebo. Non-linear SVMs and 10-fold cross-validation were applied to predict converters/non-converters (175/189) at two years follow-up based on clinical and demographic data, lesion-specific quantitative geometric features and grey-matter-to-whole-brain volume ratios. We applied linear SVM analysis and leave-one-out cross-validation to subgroups of converters (n = 25) and non-converters (n = 44) based on cortical grey matter segmentations. Highest prediction accuracies of 70.4% (p = 8e-5) were reached with a combination of lesion-specific geometric (image-based) and demographic/clinical features. Cortical grey matter was informative for the placebo group (acc.: 64.6%, p = 0.002) but not for the interferon group. Classification based on demographic/clinical covariates only resulted in an accuracy of 56% (p = 0.05). Overall, lesion geometry was more informative in the interferon group, EDSS and sex were more important for the placebo cohort. Alongside standard demographic and clinical measures, both lesion geometry and grey matter based information can aid prediction of conversion to CDMS.

Original languageEnglish
Pages (from-to)1361-1374
JournalBrain Imaging and Behavior
Issue number5
Publication statusPublished - 1 Oct 2019


  • Classification
  • Clinically isolated syndrome
  • Lesion geometry
  • MRI
  • Multiple sclerosis
  • Support vector machine

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