  Multiple Myeloma (MM) is a blood cancer implying bone marrow involvement,
renal damages and osteolytic lesions. The skeleton involvement of MM is at the
core of the present paper, exploiting radiomics and artificial intelligence to
identify image-based biomarkers for MM. Preliminary results show that MM is
associated to an extension of the intrabone volume for the whole body and that
machine learning can identify CT image features mostly correlating with the
disease evolution. This computational approach allows an automatic
stratification of MM patients relying of these biomarkers and the formulation
of a prognostic procedure for determining the disease follow-up.
