Prognostic significance of FDG-PET/CT based radiomics analysis in newly-diagnosed multiple myeloma: a comparative study with clinical assessment - Li, Fei; Jiang, Baiyang; Fu, Ye; Yu, Qingyang; Duan, Guangwen; Yan, Jiayang; Jiang, Qinling; Sun, Hongbiao; Xiao, Yi; Chen, Qi; Xu, Shaochun; Wang, Xiang; et al. (Frontiers in Oncology, 2025)
ObjectiveThis study aimed to construct and validate a fusion diagnostic model based on Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography(FDG-PET/CT) radiomics for predicting overall survival of multiple myeloma (MM) patients.MethodsA total of 199 patients newly diagnosed with MM were included from two centers. All patients underwent whole-body PET/CT scans within one month before the initiation of treatment and were followed up for over five years. Radiomic features of MM were extracted from CT images and dimensionality reduction was performed by LASSO regression analysis. Cox Proportional Hazards Model was then constructed to predict patient survival. A clinical-radiomic fusion model was constructed by integrating independent clinical risk factors, including comprehensive laboratory parameters, R-ISS, and PET functional metabolic parameters, with the radiomic model. The discrimination ability of the model was evaluated using the C-index, and it’s calibration was assessed using calibration curves.ResultsThe C-indexes for the radiomics model in the training and testing cohorts were 0.736 and 0.708, respectively; for the clinical model, they were 0.676 and 0.696, respectively; and for the integrated model, they were 0.791 and 0.776, respectively. The integrated diagnostic model outperformed both the radiomics and clinical models, showcasing higher discriminative ability and improved calibration. In the training set, the C-index was 0.791 (95% confidence interval [CI]: 0.713-0.853), with an ICI of 0.015, E50 of 0.014, and AIC of 10.987. In the testing set, the C-index was 0.776 (95% CI: 0.654–0.894), with an ICI of 0.069, E50 of 0.04, and AIC of 11.492.ConclusionsThis integrated prediction model exhibited satisfactory performance in predicting survival outcomes for patients diagnosed with MM and improved precision in discriminating between patients with a good prognosis and poor prognosis.