Predicting conversion from clinically isolated syndrome to multiple sclerosis–An imaging-based machine learning approach

Publication date: Available online 5 November 2018Source: NeuroImage: ClinicalAuthor(s): Haike Zhang, Esther Alberts, Viola Pongratz, Mark Mühlau, Claus Zimmer, Benedikt Wiestler, Paul EichingerAbstractMagnetic resonance imaging (MRI) scans play a pivotal role in the evaluation of patients presenting with a clinically isolated syndrome (CIS), as these may depict brain lesions suggestive of an inflammatory cause. We hypothesized that it is possible to predict the conversion from CIS to multiple sclerosis (MS) based on the baseline MRI scan by studying image features of these lesions.We analyzed 84 patients diagnosed with CIS from a prospective observational single center cohort. The patients were followed up for at least three years. Conversion to MS was defined according to the 2010 McDonald criteria. Brain lesions were segmented based on 3D FLAIR and 3D T1 images. We generated brain lesion masks by a computer assisted manual segmentation. We also generated a set of automated segmentations using the Lesion Segmentation Toolbox for SPM to assess the influence of different segmentation methods. Shape and brightness features were automatically calculated from the segmented masks and used as input data to train an oblique random forest classifier. Prediction accuracies of the resulting model were validated through a three-fold cross-validation.Conversion from CIS to MS occurred in 66 of 84 patients (79%). The conversion or non-conversion was predicted correctly in 71 patients ba...
Source: NeuroImage: Clinical - Category: Radiology Source Type: research