The identification of early biomarkers to predict the disability accumulation is crucial in Multiple Sclerosis (MS). We performed a combined radiomics and Machine Learning (ML) study to predict long-term clinical changes in MS. Radiomics data were extracted from data of 177 patients with a 10-years clinical follow-up available. The model based on the recursive elimination of the features combined with the Extra Trees classifier was able to obtain a maximum precision for each endpoint of 0.71 and 0.69 for cognitive and motor disability, respectively.This combined radiomics-ML approach seems to be a feasible tool for long-term clinical prediction in MS.
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