Predicting the individual multiple sclerosis (MS) patients evolution, based on markers available from disease onset, may help the neurologist in the patient care. However, such a prediction remains a challenge. In this study, we merged spatial information of fiber tracking with diffusivity metrics, measured in 68 patients presenting the three forms of MS, in order to classify patients using a white matter fiber-bundle profile analysis. The good performances of the clustering, reached with fractional anisotropy and mean diffusivity together, make our method a potential tool to better predict the disease evolution, especially the conversion of RR-MS to SP-MS.
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