ArticleBrain imaging and behavior2019
MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry.
Article in Brain imaging and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Evolution from a first clinical demyelinating event to multiple sclerosis in the REFLEX trial: Regional susceptibility in the conversion to multiple sclerosis at disease onset and its amenability to subcutaneous interferon beta-1a.European journal of neurology · 2022Trial
- Childhood absence epilepsy and distinct dynamic functional network connectivity patterns in self-limited epilepsy with centrotemporal spikes: a resting-state fMRI study.Pediatric research · 2026Article
- SeruNet-MS: A Two-Stage Interpretable Framework for Multiple Sclerosis Risk Prediction with SHAP-Based Explainability.Neurology international · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- The changes in brain network functional gradients and dynamic functional connectivity in SeLECTS patients revealing disruptive and compensatory mechanisms in brain networks.Frontiers in psychiatry · 2025Article
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Current and future role of MRI in the diagnosis and prognosis of multiple sclerosis.The Lancet regional health. Europe · 2024Review
- AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans.Scientific reports · 2024Article
- Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing.Zeitschrift fur medizinische Physik · 2024Article
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective.Journal of neurology · 2023Review
- Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set.Human brain mapping · 2023Article
- Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis.Frontiers in neurology · 2023Article
- Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.Neuroradiology · 2022Article
- Altered voxel-mirrored homotopic connectivity in right temporal lobe epilepsy as measured using resting-state fMRI and support vector machine analyses.Frontiers in psychiatry · 2022Article
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence.Neurology · 2021Review
- Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images.Frontiers in immunology · 2021Review
- Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value.Scientific reports · 2019Article
- SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis.NeuroImage. Clinical · 2019Article
Corrections and comments
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Authors and funding
24 authors.
Funding
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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