Establishment of a Multimodal Prognostic Prediction Model for Multiple Myeloma Patients Based on Radiomics and Clinical Features: A Retrospective Cohort Study

Objective: We aimed to develop a survival prediction system integrating radiomics and clinical features for newly diagnosed multiple myeloma (MM), and to compare the performance of different feature sets and algorithms in early prediction of progression-free survival (PFS). Methods: This study retrospectively included 300 MM patients between June 2022 and June 2024, with their baseline positron emission tomography, computed tomography, and magnetic resonance imaging radiomics features and clinical variables collected. Following construction of radiomics-based risk score (Rad-score), seven machine learning-based survival models were established using the clinical feature set, the image feature set and the integrated feature set. Results: The fusion feature set-based GBM model showed numerically favorable overall performance in predicting 12-month PFS, suggesting that the integration of radiomics and clinical variables may provide complementary predictive information for early risk stratification. The model effectively distinguished high, intermediate and low-risk patients (log-rank P<0.001), and the calibration curve analysis and DCA revealed favorable calibration and high clinical net benefits. SHAP analysis showed that the Rad-score was one of the most important features in the model, indicating that radiomics information may contribute prognostic value within the integrated feature space. β2-Microglobulin, age, lactate dehydrogenase, blood calcium, platelet count, and hemoglobin were also identified as key contributing features. Conclusion: The integration of radiomics and clinical variables may improve early PFS prediction in MM patients. The GBM model using fused features showed relatively good discriminative ability, potential clinical applicability, and favorable interpretability.