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Semi-quantitative FDG parameters predict survival in multiple myeloma patients without autologous stem cell transplantation
- Source :
- Cancer Imaging, Vol 23, Iss 1, Pp 1-12 (2023)
- Publication Year :
- 2023
- Publisher :
- BMC, 2023.
-
Abstract
- Abstract Background F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is useful in multiple myeloma (MM) for initial workup and treatment response evaluation. Herein, we evaluated the prognostic value of semi-quantitative FDG parameters for predicting the overall survival (OS) of MM patients with or without autologous stem cell transplantation (ASCT). Methods Study subjects comprised 227 MM patients who underwent baseline FDG PET/CT. Therein, 123 underwent ASCT while 104 did not. Volumes of interest (VOIs) of bones were drawn on CT images using a threshold of 150 Hounsfield units. FDG parameters of maximum standardized uptake value (SUVmax), mean SUV (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and number of focal lesions (FLs) were measured. Kaplan-Meier survival analysis with log-rank tests and Cox proportional hazards regression analyses were performed for overall survival (OS). Results In the ASCT cohort, R-ISS stage, MTV, and TLG were associated with survival. In the non-ASCT cohort, however, R-ISS stage was not associated with patient outcomes. In contrast, high SUVmax, SUVmean, MTV, TLG, and FL could predict worse OS (hazard ratio [HR] = 2.569, 2.649, 2.506, 2.839, and 1.988, respectively). Importantly, combining FDG parameters with R-ISS stage provided a new risk classification system that discriminated worse OS in the non-ASCT cohort significantly better than did R-ISS stage alone. Conclusions In the non-ASCT cohort, semi-quantitative FDG parameters were significant predictors of worse OS. Furthermore, combining FDG parameters with R-ISS stage may provide a new risk staging system that can better stratify the survival of MM patients without ASCT.
Details
- Language :
- English
- ISSN :
- 14707330
- Volume :
- 23
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Cancer Imaging
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.63e0afc4324f1790f2c44889b75856
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s40644-023-00625-z