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

TL;DR

This paper explores the use of radiomics and AI on CT scans to identify prognostic biomarkers in multiple myeloma, enabling automatic patient stratification and disease follow-up prediction.

Contribution

It introduces a novel computational approach combining radiomics and machine learning to identify image-based biomarkers for multiple myeloma prognosis.

Findings

MM is linked to increased intrabone volume.

Machine learning identifies CT features correlating with disease progression.

The approach enables automatic patient stratification.

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.