SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis

•RFE-SVMs predict future outcome of CIS patients with conservative accuracy estimates between 64.9% and 88.1%.•Recursive feature selection improves classification performance compared to using all information.•Relevant features include regional WM lesion load and GM density, as well as the type of C...

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Vydáno v:NeuroImage clinical Ročník 24; s. 102011
Hlavní autoři: Wottschel, Viktor, Chard, Declan T., Enzinger, Christian, Filippi, Massimo, Frederiksen, Jette L., Gasperini, Claudio, Giorgio, Antonio, Rocca, Maria A., Rovira, Alex, De Stefano, Nicola, Tintoré, Mar, Alexander, Daniel C., Barkhof, Frederik, Ciccarelli, Olga
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier Inc 01.01.2019
Elsevier
Témata:
Adolescent
Adult
Disease Progression
Feature selection
Female
Follow-Up Studies
Gray Matter - diagnostic imaging
Humans
Image Processing, Computer-Assisted - methods
Machine learning classification
Magnetic Resonance Imaging - methods
Male
Middle Aged
Multiple sclerosis
Multiple Sclerosis - diagnostic imaging
Neuroimaging - methods
Radiology
Regular
Retrospective Studies
Support Vector Machine
White Matter - diagnostic imaging
Young Adult
Machine learning classification
Multiple sclerosis
Feature selection
ISSN:2213-1582, 2213-1582
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Shrnutí:•RFE-SVMs predict future outcome of CIS patients with conservative accuracy estimates between 64.9% and 88.1%.•Recursive feature selection improves classification performance compared to using all information.•Relevant features include regional WM lesion load and GM density, as well as the type of CIS onset.•Cross-validation introduces positive bias on accuracy estimate. Machine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.
Bibliografie:ObjectType-Article-1
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ISSN:2213-1582
2213-1582
DOI:10.1016/j.nicl.2019.102011