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

Daniela Schenone; 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 Stefano Tagliafico

doi:10.1117/12.2548983

16 March 2020 Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma

Daniela Schenone, 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 Stefano Tagliafico

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Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113144A (2020) https://doi.org/10.1117/12.2548983
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

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.

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Daniela Schenone, 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, and Alberto Stefano Tagliafico "Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113144A (16 March 2020); https://doi.org/10.1117/12.2548983

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