@inproceedings{6457bf033b6d44b0a5ef1409d9ea81f1,
title = "Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma",
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.",
keywords = "clustering, image features, image segmentation, x-ray ct",
author = "Daniela Schenone and Rita Lai and Michele Cea and Federica Rossi and Lorenzo Torri and Bianca Bignotti and Giulia Succio and Stefano Gualco and Alessio Conte and Alida Dominietto and Massone, {Anna Maria} and Michele Piana and Cristina Campi and Francesco Frassoni and Gianmario Sambuceti and Tagliafico, {Alberto Stefano}",
year = "2020",
doi = "10.1117/12.2548983",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Hahn, {Horst K.} and Mazurowski, {Maciej A.}",
booktitle = "Medical Imaging 2020",
address = "United States",
note = "Medical Imaging 2020: Computer-Aided Diagnosis ; Conference date: 16-02-2020 Through 19-02-2020",
}