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Abstract #0778

“Better together”: a combination of machine learning and radiomics to predict long-term disability in Multiple Sclerosis

Sirio Cocozza1, Renato Cuocolo1, Giuseppe Pontillo1, Lorenzo Ugga1, Maria Petracca1, Teresa Costabile1, Roberta Lanzillo1, Vincenzo Brescia Morra1, Mario Quarantelli2, and Arturo Brunetti1
1University of Naples Federico II, Naples, Italy, 2National Research Council, Naples, Italy


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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