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

• DOI: 10.48550/arxiv.2001.08924
• Publication Date: 2020-01-01
• Authors: Daniela Schenonea; Rita Lai; Michele Cea; Federica Rossi; Lorenzo Torri; Bianca Bignotti; Giulia Succio; Stefano Gualco; Alessio Conte; Alida Dominietto; Anna Maria Massone; Michele Piana; Cristina Campi; Francesco Frassoni; Gianmario Sambuceti; Alberto Tagliafico
• Keywords: Identification

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