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

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 proceeding › Conference 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 language	English
Title of host publication	Medical Imaging 2020
Subtitle of host publication	Computer-Aided Diagnosis
Editors	Horst K. Hahn, Maciej A. Mazurowski
Publisher	SPIE
ISBN (Electronic)	9781510633957
DOIs	https://doi.org/10.1117/12.2548983
Publication status	Published - 2020
Event	Medical Imaging 2020: Computer-Aided Diagnosis - Houston, United States Duration: Feb 16 2020 → Feb 19 2020

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	11314
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2020: Computer-Aided Diagnosis
Country	United States
City	Houston
Period	2/16/20 → 2/19/20

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

Access to Document

10.1117/12.2548983

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Schenone, D., Lai, R., Cea, M., Rossi, F., Torri, L., Bignotti, B., Succio, G., Gualco, S., Conte, A., Dominietto, A., Massone, A. M., Piana, M., Campi, C., Frassoni, F., Sambuceti, G., & Tagliafico, A. S. (2020). Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma. In H. K. Hahn, & M. A. Mazurowski (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis [113144A] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2548983

Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma. / Schenone, Daniela; Lai, Rita; Cea, Michele; Rossi, Federica; Torri, Lorenzo; Bignotti, Bianca; Succio, Giulia; Gualco, Stefano; Conte, Alessio; Dominietto, Alida; Massone, Anna Maria; Piana, Michele; Campi, Cristina; Frassoni, Francesco ; Sambuceti, Gianmario ; Tagliafico, Alberto Stefano.

Medical Imaging 2020: Computer-Aided Diagnosis. ed. / Horst K. Hahn; Maciej A. Mazurowski. SPIE, 2020. 113144A (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Schenone, D, Lai, R, Cea, M, Rossi, F, Torri, L, Bignotti, B, Succio, G, Gualco, S, Conte, A, Dominietto, A, Massone, AM, Piana, M, Campi, C, Frassoni, F , Sambuceti, G & Tagliafico, AS 2020, Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma. in HK Hahn & MA Mazurowski (eds), Medical Imaging 2020: Computer-Aided Diagnosis., 113144A, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11314, SPIE, Medical Imaging 2020: Computer-Aided Diagnosis, Houston, United States, 2/16/20. https://doi.org/10.1117/12.2548983

Schenone D, Lai R, Cea M, Rossi F, Torri L, Bignotti B et al. Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma. In Hahn HK, Mazurowski MA, editors, Medical Imaging 2020: Computer-Aided Diagnosis. SPIE. 2020. 113144A. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2548983

Schenone, Daniela ; Lai, Rita ; Cea, Michele ; Rossi, Federica ; Torri, Lorenzo ; Bignotti, Bianca ; Succio, Giulia ; Gualco, Stefano ; Conte, Alessio ; Dominietto, Alida ; Massone, Anna Maria ; Piana, Michele ; Campi, Cristina ; Frassoni, Francesco ; Sambuceti, Gianmario ; Tagliafico, Alberto Stefano. / Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma. Medical Imaging 2020: Computer-Aided Diagnosis. editor / Horst K. Hahn ; Maciej A. Mazurowski. SPIE, 2020. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).

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AB - 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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