Predicting outcome in clinically isolated syndrome using machine learning

We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clinically isolated syndrome (CIS), on the basis of sin...

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Bibliographic Details
Published in:NeuroImage clinical 2015-01, Vol.7 (C), p.281-287
Main Authors: Wottschel, V, Alexander, D C, Kwok, P P, Chard, D T, Stromillo, M L, De Stefano, N, Thompson, A J, Miller, D H, Ciccarelli, O
Format: Article
Language:English
Subjects:
Adult
Clinically isolated syndrome
Demyelinating Diseases - diagnosis
Disease Progression
Female
Humans
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Male
Middle Aged
MRI
Multiple Sclerosis
Multiple Sclerosis - diagnosis
Regular
Retrospective Studies
Sensitivity and Specificity
Support Vector Machine
Support vector machines
Young Adult
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Summary:We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clinically isolated syndrome (CIS), on the basis of single patient's lesion features and clinical/demographic characteristics. Seventy-four patients at onset of CIS were scanned and clinically reviewed after one and three years. CDMS was used as the gold standard against which SVM classification accuracy was tested. Radiological features related to lesional characteristics on conventional MRI were defined a priori and used in combination with clinical/demographic features in an SVM. Forward recursive feature elimination with 100 bootstraps and a leave-one-out cross-validation was used to find the most predictive feature combinations. 30 % and 44 % of patients developed CDMS within one and three years, respectively. The SVMs correctly predicted the presence (or the absence) of CDMS in 71.4 % of patients (sensitivity/specificity: 77 %/66 %) at 1 year, and in 68 % (60 %/76 %) at 3 years on average over all bootstraps. Combinations of features consistently gave a higher accuracy in predicting outcome than any single feature. Machine-learning-based classifications can be used to provide an "individualised" prediction of conversion to MS from subjects' baseline scans and clinical characteristics, with potential to be incorporated into routine clinical practice.
ISSN:2213-1582
2213-1582
DOI:10.1016/j.nicl.2014.11.021