Purpose: To provide a comprehensive overview of the current literature on the applications of PET-based radiomics in patients affected by multiple myeloma (MM) and FDG-avid lymphomas. Methods: Relevant studies on the topic were selected by searching Pubmed/Medline. Retrospective or prospective cohort studies focusing on the clinical applications of PET-radiomics in lymphomas and MM were retrieved, analyzed, and discussed. Result: A total of 17 papers were ultimately selected, with 9 focusing on non-Hodgkin lymphomas, 6 on Hodgkin lymphomas, and 5 dealing with MM. Machine learning-derived models incorporating first-, second-, and third-order radiomic features extracted from baseline PET/CT scans demonstrated promising results in predicting outcomes, specifically the 2-year event-free survival (EFS) in lymphomas. Furthermore, models based on PET-radiomic features were effective in distinguishing between MM and bone metastases, as well as in assessing minimal residual disease, outperforming visual analysis. Conclusion: Preliminary results suggest that PET-radiomic features, which reflect the biological heterogeneity and spatial distribution of lesions, may play a prognostic role in both lymphomas and MM. Nevertheless, before implementing these findings in clinical practice, it is imperative to standardize the methodological approaches and validate them in large prospective trials.
Pet-radiomics in lymphoma and multiple myeloma: update of current literature / Filippi, L.; Ferrari, C.; Nuvoli, S.; Bianconi, F.; Donner, D.; Marongiu, A.; Mammucci, P.; Vultaggio, V.; Chierichetti, F.; Rubini, G.; Spanu, A.; Schillaci, O.; Palumbo, B.; Evangelista, L.. - In: CLINICAL AND TRANSLATIONAL IMAGING. - ISSN 2281-5872. - (2023). [10.1007/s40336-023-00604-1]
Pet-radiomics in lymphoma and multiple myeloma: update of current literature
Ferrari C.;Nuvoli S.;Marongiu A.;Spanu A.;Palumbo B.;
2023-01-01
Abstract
Purpose: To provide a comprehensive overview of the current literature on the applications of PET-based radiomics in patients affected by multiple myeloma (MM) and FDG-avid lymphomas. Methods: Relevant studies on the topic were selected by searching Pubmed/Medline. Retrospective or prospective cohort studies focusing on the clinical applications of PET-radiomics in lymphomas and MM were retrieved, analyzed, and discussed. Result: A total of 17 papers were ultimately selected, with 9 focusing on non-Hodgkin lymphomas, 6 on Hodgkin lymphomas, and 5 dealing with MM. Machine learning-derived models incorporating first-, second-, and third-order radiomic features extracted from baseline PET/CT scans demonstrated promising results in predicting outcomes, specifically the 2-year event-free survival (EFS) in lymphomas. Furthermore, models based on PET-radiomic features were effective in distinguishing between MM and bone metastases, as well as in assessing minimal residual disease, outperforming visual analysis. Conclusion: Preliminary results suggest that PET-radiomic features, which reflect the biological heterogeneity and spatial distribution of lesions, may play a prognostic role in both lymphomas and MM. Nevertheless, before implementing these findings in clinical practice, it is imperative to standardize the methodological approaches and validate them in large prospective trials.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
