{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T21:31:31Z","timestamp":1785447091000,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:00:00Z","timestamp":1702425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/jcm12247669","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T12:00:37Z","timestamp":1702468837000},"page":"7669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art"],"prefix":"10.3390","volume":"12","author":[{"given":"Luigi","family":"Manco","sequence":"first","affiliation":[{"name":"Medical Physics Unit, Azienda USL of Ferrara, 45100 Ferrara, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0810-6494","authenticated-orcid":false,"given":"Domenico","family":"Albano","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Department, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3007-3898","authenticated-orcid":false,"given":"Luca","family":"Urso","sequence":"additional","affiliation":[{"name":"Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mattia","family":"Arnaboldi","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Unit, Fondazione IRCCS Ca\u2019 Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9217-3456","authenticated-orcid":false,"given":"Massimo","family":"Castellani","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Unit, Fondazione IRCCS Ca\u2019 Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5254-6852","authenticated-orcid":false,"given":"Luigia","family":"Florimonte","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Unit, Fondazione IRCCS Ca\u2019 Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9535-9152","authenticated-orcid":false,"given":"Gabriele","family":"Guidi","sequence":"additional","affiliation":[{"name":"Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Turra","sequence":"additional","affiliation":[{"name":"Medical Physics Unit, Azienda USL of Ferrara, 45100 Ferrara, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5868-3372","authenticated-orcid":false,"given":"Angelo","family":"Castello","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Unit, Fondazione IRCCS Ca\u2019 Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9222-8752","authenticated-orcid":false,"given":"Stefano","family":"Panareo","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Unit, Department of Oncology and Hematology, University Hospital of Modena, Via del Pozzo 71, 41124 Modena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21332","article-title":"Cancer statistics","volume":"66","author":"Siegel","year":"2016","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17046","DOI":"10.1038\/nrdp.2017.46","article-title":"Multiple myeloma","volume":"3","author":"Kumar","year":"2017","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e538","DOI":"10.1016\/S1470-2045(14)70442-5","article-title":"International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma","volume":"15","author":"Rajkumar","year":"2014","journal-title":"Lancet Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.6004\/jnccn.2020.0057","article-title":"Multiple Myeloma, Version 3.2021, NCCN Clinical Practice Guidelines in Oncology","volume":"18","author":"Kumar","year":"2020","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1007\/s11604-018-0738-z","article-title":"Diagnostic performance of 18F-FDG PET\/CT and whole-body MRI before and early after treatment of multiple myeloma: A prospective comparative study","volume":"36","author":"Basha","year":"2018","journal-title":"Jpn. J. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1007\/s00259-015-3200-9","article-title":"Image interpretation criteria for FDG PET\/CT in multiple myeloma: A new proposal from an Italian expert panel. IMPeTUs (Italian Myeloma criteria for PET USe)","volume":"43","author":"Nanni","year":"2016","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1038\/s41408-020-00359-2","article-title":"Multiple myeloma current treatment algorithms","volume":"10","author":"Rajkumar","year":"2020","journal-title":"Blood Cancer J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging-\"how-to\" guide and critical reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Urso, L., Manco, L., Castello, A., Evangelista, L., Guidi, G., Castellani, M., Florimonte, L., Cittanti, C., Turra, A., and Panareo, S. (2022). PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms232113409"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Evangelista, L., Fiz, F., Laudicella, R., Bianconi, F., Castello, A., Guglielmo, P., Liberini, V., Manco, L., Frantellizzi, V., and Giordano, A. (2023). PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature. Cancers, 15.","DOI":"10.20944\/preprints202305.0801.v1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ymeth.2020.05.022","article-title":"Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework","volume":"188","author":"Ibrahim","year":"2021","journal-title":"Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep Learning in Medical Imaging: General Overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compbiomed.2018.05.018","article-title":"Survey on deep learning for radiotherapy","volume":"98","author":"Meyer","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.ejmp.2021.03.026","article-title":"Basic of machine learning and deep learning in imaging for medical physicists","volume":"83","author":"Manco","year":"2021","journal-title":"Phys. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: The bridge between medical 528 imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhong, H., Huang, D., Wu, J., Chen, X., Chen, Y., and Huang, C. (2023). 18F-FDG PET\/CT based radiomics features improve prediction of prognosis: Multiple machine learning algorithms and multimodality applications for multiple myeloma. BMC Med. Imaging, 23.","DOI":"10.1186\/s12880-023-01033-2"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1007\/s13246-023-01265-0","article-title":"Radiomics analysis of bone marrow biopsy locations in [18F]FDG PET\/CT images for measurable residual disease assessment in multiple myeloma","volume":"46","author":"Milara","year":"2023","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ni, B., Huang, G., Huang, H., Wang, T., Han, X., Shen, L., Chen, Y., and Hou, J. (2023). Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET\/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study. J. Clin. Med., 12.","DOI":"10.3390\/jcm12062280"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"217","DOI":"10.4103\/ijnm.ijnm_111_21","article-title":"Radiomic Analysis of Positron-Emission Tomography and Computed Tomography Images to Differentiate between Multiple Myeloma and Skeletal Metastases","volume":"37","author":"Mannam","year":"2022","journal-title":"Indian J. Nucl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Milara, E., G\u00f3mez-Grande, A., Tom\u00e1s-Soler, S., Seiffert, A.P., Alonso, R., G\u00f3mez, E.J., Mart\u00ednez-L\u00f3pez, J., and S\u00e1nchez-Gonz\u00e1lez, P. (2022). Bone marrow segmentation and radiomics analysis of [18F]FDG PET\/CT images for measurable residual disease assessment in multiple myeloma. Comput. Methods Programs Biomed., 225.","DOI":"10.1016\/j.cmpb.2022.107083"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"874847","DOI":"10.3389\/fmed.2022.874847","article-title":"Application of 18F-FDG PET-CT Images Based Radiomics in Identifying Vertebral Multiple Myeloma and Bone Metastases","volume":"9","author":"Jin","year":"2022","journal-title":"Front. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1097\/MNM.0000000000001437","article-title":"Improved 18-FDG PET\/CT diagnosis of multiple myeloma diffuse disease by radiomics analysis","volume":"42","author":"Mesguich","year":"2021","journal-title":"Nucl. Med. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.clml.2021.03.014","article-title":"Progression to Symptomatic Multiple Myeloma Predicted by Texture Analysis-Derived Parameters in Patients without Focal Disease at 18F-FDG PET\/CT","volume":"21","author":"Ripani","year":"2021","journal-title":"Clin. Lymphoma Myeloma Leuk."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s00259-020-05049-6","article-title":"Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: A combined analysis of two independent prospective European trials","volume":"48","author":"Jamet","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2391925","DOI":"10.1155\/2018\/2391925","article-title":"Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET\/CT Imaging Using Deep Learning Methods","volume":"2018","author":"Xu","year":"2018","journal-title":"Contrast Media Mol. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3199","DOI":"10.1001\/jama.290.24.3199","article-title":"Whole-body dual-modality PET\/CT and whole-body MRI for tumor staging in oncology","volume":"290","author":"Antoch","year":"2003","journal-title":"JAMA"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2863","DOI":"10.1200\/JCO.2015.61.2267","article-title":"Revised International Staging System for Multiple Myeloma: A Report from International Myeloma Working Group","volume":"33","author":"Palumbo","year":"2015","journal-title":"J. Clin. Oncol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Castello, A., Castellani, M., Florimonte, L., Urso, L., Mansi, L., and Lopci, E. (2022). The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria. J. Clin. Med., 11.","DOI":"10.3390\/jcm11061740"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Joshi, G., Jain, A., Araveeti, S.R., Adhikari, S., Garg, H., and Bhandari, M. (medRxiv, 2022). FDA approved Artificial Intelligence and Machine Learning (AI\/ML)-Enabled Medical Devices: An updated landscape, medRxiv, preprint.","DOI":"10.1101\/2022.12.07.22283216"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Filippi, L., Ferrari, C., Nuvoli, S., Bianconi, F., Donner, D., Marongiu, A., Mammucci, P., Vultaggio, V., Chierichetti, F., and Rubini, G. (Clin. Transl. Imaging, 2023). Pet-radiomics in lymphoma and multiple myeloma: Update of current literature, Clin. Transl. Imaging, preprint.","DOI":"10.1007\/s40336-023-00604-1"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1002\/ajh.25507","article-title":"Imaging and bone marrow assessments improve minimal residual disease prediction in multiple myeloma","volume":"94","author":"Alonso","year":"2019","journal-title":"Am. J. Hematol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1038\/s41375-018-0329-0","article-title":"Combination of flow cytometry and functional imaging for monitoring of residual disease in myeloma","volume":"33","author":"Rasche","year":"2019","journal-title":"Leukemia"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16429","DOI":"10.1038\/s41598-019-52740-2","article-title":"Proposal for a Quantitative 18F-FDG PET\/CT Metabolic Parameter to Assess the Intensity of Bone Involvement in Multiple Myeloma","volume":"9","author":"Takahashi","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"172","DOI":"10.2967\/jnumed.121.262464","article-title":"A Guide to Com-Bat Harmonization of Imaging Biomarkers in Multicenter Studies","volume":"63","author":"Orlhac","year":"2022","journal-title":"J. Nucl. Med."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/12\/24\/7669\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:38:22Z","timestamp":1760132302000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/12\/24\/7669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,13]]},"references-count":36,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["jcm12247669"],"URL":"https:\/\/doi.org\/10.3390\/jcm12247669","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,13]]}}}