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

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 conver...

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Glavni autori: Bendfeldt, K, Taschler, B, Gaetano, L, Madoerin, P, Kuster, P, Mueller-Lenke, N, Amann, M, Vrenken, H, Wottschel, V, Barkhof, F, Borgwardt, S, Klöppel, S, Wicklein, EM, Kappos, L, Edan, G, Freedman, MS, Montalbán, X, Hartung, HP, Pohl, C, Sandbrink, R, Sprenger, T, Radue, EW, Wuerfel, J, Nichols, TE
Format: Journal article
Izdano: Springer US 2018
_version_ 1826297936331407360
author Bendfeldt, K
Taschler, B
Gaetano, L
Madoerin, P
Kuster, P
Mueller-Lenke, N
Amann, M
Vrenken, H
Wottschel, V
Barkhof, F
Borgwardt, S
Klöppel, S
Wicklein, EM
Kappos, L
Edan, G
Freedman, MS
Montalbán, X
Hartung, HP
Pohl, C
Sandbrink, R
Sprenger, T
Radue, EW
Wuerfel, J
Nichols, TE
author_facet Bendfeldt, K
Taschler, B
Gaetano, L
Madoerin, P
Kuster, P
Mueller-Lenke, N
Amann, M
Vrenken, H
Wottschel, V
Barkhof, F
Borgwardt, S
Klöppel, S
Wicklein, EM
Kappos, L
Edan, G
Freedman, MS
Montalbán, X
Hartung, HP
Pohl, C
Sandbrink, R
Sprenger, T
Radue, EW
Wuerfel, J
Nichols, TE
author_sort Bendfeldt, K
collection OXFORD
description 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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spelling oxford-uuid:d10a7d51-3aa5-4f6c-a298-c44c560c69ff2022-03-27T07:54:10ZMRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometryJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d10a7d51-3aa5-4f6c-a298-c44c560c69ffSymplectic Elements at OxfordSpringer US2018Bendfeldt, KTaschler, BGaetano, LMadoerin, PKuster, PMueller-Lenke, NAmann, MVrenken, HWottschel, VBarkhof, FBorgwardt, SKlöppel, SWicklein, EMKappos, LEdan, GFreedman, MSMontalbán, XHartung, HPPohl, CSandbrink, RSprenger, TRadue, EWWuerfel, JNichols, TENeuroanatomical 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.
spellingShingle Bendfeldt, K
Taschler, B
Gaetano, L
Madoerin, P
Kuster, P
Mueller-Lenke, N
Amann, M
Vrenken, H
Wottschel, V
Barkhof, F
Borgwardt, S
Klöppel, S
Wicklein, EM
Kappos, L
Edan, G
Freedman, MS
Montalbán, X
Hartung, HP
Pohl, C
Sandbrink, R
Sprenger, T
Radue, EW
Wuerfel, J
Nichols, TE
MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry
title MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry
title_full MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry
title_fullStr MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry
title_full_unstemmed MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry
title_short MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry
title_sort mri based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using svm and lesion geometry
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