Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art

Author:

Manco Luigi1,Albano Domenico2ORCID,Urso Luca3ORCID,Arnaboldi Mattia4,Castellani Massimo4ORCID,Florimonte Luigia4ORCID,Guidi Gabriele5ORCID,Turra Alessandro1,Castello Angelo4ORCID,Panareo Stefano6ORCID

Affiliation:

1. Medical Physics Unit, Azienda USL of Ferrara, 45100 Ferrara, Italy

2. Nuclear Medicine Department, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy

3. Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy

4. Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy

5. Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy

6. Nuclear Medicine Unit, Department of Oncology and Hematology, University Hospital of Modena, Via del Pozzo 71, 41124 Modena, Italy

Abstract

Multiple myeloma (MM) is a heterogeneous neoplasm accounting for the second most prevalent hematologic disorder. The identification of noninvasive, valuable biomarkers is of utmost importance for the best patient treatment selection, especially in heterogeneous diseases like MM. Despite molecular imaging with positron emission tomography (PET) has achieved a primary role in the characterization of MM, it is not free from shortcomings. In recent years, radiomics and artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL) algorithms, have played an important role in mining additional information from medical images beyond human eyes’ resolving power. Our review provides a summary of the current status of radiomics and AI in different clinical contexts of MM. A systematic search of PubMed, Web of Science, and Scopus was conducted, including all the articles published in English that explored radiomics and AI analyses of PET/CT images in MM. The initial results have highlighted the potential role of such new features in order to improve the clinical stratification of MM patients, as well as to increase their clinical benefits. However, more studies are warranted before these approaches can be implemented in clinical routines.

Publisher

MDPI AG

Subject

General Medicine

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