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

Brain Imaging and Behavior
Kerstin BendfeldtThomas E Nichols

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/cl...Continue Reading

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Citations

Jul 10, 2020·RöFo : Fortschritte auf dem Gebiete der Röntgenstrahlen und der Nuklearmedizin·Paul EichingerBenedikt Wiestler
Nov 15, 2019·Multiple Sclerosis Journal - Experimental, Translational and Clinical·Marco Tk LawRoger Tam
Jun 23, 2020·Multiple Sclerosis and Related Disorders·Jan KolčavaYvonne Benešová
Aug 31, 2021·Frontiers in Immunology·Faezeh MoazamiVassili Soumelis

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Methods Mentioned

BETA
PCA

Software Mentioned

Matlab
FMRIBs
Images
Show
LIBSVM
DARTEL
BENEFIT
libray FSL

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