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

Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma