  Background and purpose: The unanticipated detection by magnetic resonance
imaging (MRI) in the brain of asymptomatic subjects of white matter lesions
suggestive of multiple sclerosis (MS) has been named radiologically isolated
syndrome (RIS). As the difference between early MS [i.e. clinically isolated
syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to
improve detection of the subclinical form without interfering with MRI as there
are radiological diagnostic criteria for that. Our objective was to use
machine-learning classification methods to identify morphometric measures that
help to discriminate patients with RIS from those with CIS.
  Methods: We used a multimodal 3-T MRI approach by combining MRI biomarkers
(cortical thickness, cortical and subcortical grey matter volume, and white
matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS
for single-subject level classification.
  Results: The best proposed models to predict the diagnosis of CIS and RIS
were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers
using only three features: the left rostral middle frontal gyrus volume and the
fractional anisotropy values in the right amygdala and right lingual gyrus. The
Naive Bayes obtained the highest accuracy [overall classification, 0.765; area
under the receiver operating characteristic (AUROC), 0.782].
  Conclusions: A machine-learning approach applied to multimodal MRI data may
differentiate between the earliest clinical expressions of MS (CIS and RIS)
with an accuracy of 78%.
  Keywords: Bagging; Multilayer Perceptron; Naive Bayes classifier; clinically
isolated syndrome; diffusion tensor imaging; machine-learning; magnetic
resonance imaging; multiple sclerosis; radiologically isolated syndrome.
