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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
Publication statusPublished - 2020
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period2/16/202/19/20

Keywords

  • clustering
  • image features
  • image segmentation
  • x-ray ct

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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