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

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Abstract

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.

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Notes

  1. With other data sets that comprise of larger numbers of lesions per patients, we found that the segmentation into white matter track regions can substantially increase prediction accuracies compared to those feature sets that rely on whole brain summaries only.

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Author notes

  1. Bernd Taschler contributed equally to this work.

Authors and Affiliations

  1. Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland

    Kerstin Bendfeldt, Laura Gaetano, Philip Madoerin, Pascal Kuster, Nicole Mueller-Lenke, Michael Amann, Stefan Borgwardt, Till Sprenger, Ernst-Wilhelm Radue & Jens Wuerfel

  2. German Center for Neurodegenerative Diseases, Bonn, Germany

    Bernd Taschler

  3. Department of Statistics, University of Warwick, Coventry, UK

    Bernd Taschler & Thomas E. Nichols

  4. Department of Neurology, University Hospital Basel, Basel, Switzerland

    Laura Gaetano, Michael Amann, Ludwig Kappos & Till Sprenger

  5. VU University Medical Center, Amsterdam, The Netherlands

    Hugo Vrenken, Viktor Wottschel & Frederik Barkhof

  6. Institutes of Neurology and Healthcare Engineering, UCL, London, UK

    Frederik Barkhof

  7. Department of Psychiatry (1), University of Basel, Basel, Switzerland

    Stefan Borgwardt

  8. King’s College London, Department of Psychosis Studies, Institute of Psychiatry, London, UK

    Stefan Borgwardt

  9. Department of Psychiatry and Psychotherapy, Freiburg Brain Imaging, University Medical Center Freiburg, Freiburg, Germany

    Stefan Klöppel

  10. Bayer Pharma AG, Berlin, Germany

    Eva-Maria Wicklein, Christoph Pohl & Rupert Sandbrink

  11. CHU-Hopital Pontchaillou, Rennes, France

    Gilles Edan

  12. University of Ottawa and Ottawa Hospital Research Institute, Ottawa, ON, Canada

    Mark S. Freedman

  13. Hospital Universitari Vall d’Hebron, Barcelona, Spain

    Xavier Montalbán

  14. Department of Neurology, Heinrich-Heine Universität, Düsseldorf, Germany

    Hans-Peter Hartung & Rupert Sandbrink

  15. Charité University Medicine Berlin, Berlin, Germany

    Christoph Pohl & Jens Wuerfel

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  1. Kerstin Bendfeldt
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  2. Bernd Taschler
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Corresponding author

Correspondence to Kerstin Bendfeldt.

Ethics declarations

Conflict of interest

Kerstin Bendfeldt declares that she has no conflict of interest.

Bernd Taschler declares that he has no conflict of interest.

Laura Gaetano declares that she has no conflict of interest.

Philip Madoerin declares that he has no conflict of interest.

Pascal Kuster declares that he has no conflict of interest.

Nicole Mueller-Lenke declares that she has no conflict of interest.

Michael Amann declares that he has no conflict of interest.

Hugo Vrenken declares that he has no conflict of interest.

Viktor Wottschel declares that he has no conflict of interest.

Frederik Barkhof declares that he has no conflict of interest.

Stefan Borgwardt declares that he has no conflict of interest.

Stefan Klöppel declares that he has no conflict of interest.

Eva-Maria Wicklein declares that she has no conflict of interest.

Ludwig Kappos declares that he has no conflict of interest.

Gilles Edan declares that he has no conflict of interest.

Mark S. Freedman declares that he has no conflict of interest.

Xavier Montalbán declares that he has no conflict of interest.

Hans-Peter Hartung declares that he has no conflict of interest.

Christoph Pohl ✝ - no conflict of interest.

Rupert Sandbrink declares that he has no conflict of interest.

Rupert Sandbrink declares that he has no conflict of interest.

Bernd Taschler declares that he has no conflict of interest.

Till Sprenger has received research grants from the Swiss MS Society, Swiss National Research Foundation, EFIC-Grünenthal and Novartis Pharmaceuticals Switzerland. Till Sprengers current and/or previous employers have received compensation for consultation and speaking activities from Mitsubishi Pharma, Eli Lilly, Sanofi Genzyme, Novartis, ATI, Actelion, Electrocore, Biogen Idec, Teva and Allergan.

Ernst-Wilhelm Radue declares that he has no conflict of interest.

Jens Wuerfel declares that he has no conflict of interest.

Thomas E. Nichols declares that he has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Bendfeldt, K., Taschler, B., Gaetano, L. et al. MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry. Brain Imaging and Behavior 13, 1361–1374 (2019). https://doi.org/10.1007/s11682-018-9942-9

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