Prediction of ISS and R-ISS Stratification in Newly Diagnosed Multiple Myeloma Using Lumbar Spine MRI Radiomics Model: A Two-Center Multimodal Study.

To address the limited availability of genetic testing, this study aimed to develop lumbar MRI-radiomics models to predict International Staging System/Revised International Staging System (ISS/R-ISS) stages in newly diagnosed multiple myeloma (ndMM). This two-center retrospective study analyzed 164 ndMM patients. Radiomics models were developed based on single or dual sequence multi-mobility features from T1-weighted imaging (T1-WI) and T2-weighted fat-suppressed (T2-FS) images. A clinical model was also constructed as the baseline for comparison. A fusion model combining optimal radiomics features and peripheral blood biomarkers was subsequently compared against both the clinical model and the radiomics models. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity across training, internal, and external test sets. Independent risk factors were identified via two-step logistic regression. Differences in AUC values were compared using the DeLong test, while the net reclassification improvement (NRI) was applied to assess reclassification performance. The T1_WL model proved to be the most effective for ISS stratification (AUCs: 0.743 internal, 0.707 external), while the cross-region model showed superior predictive power for R-ISS stratification (AUCs: 0.814 internal, 0.763 external). The fusion model demonstrated significantly superior performance compared to both the radiomics model (P < 0.001) and clinical model (P < 0.001), achieving the highest AUCs of 0.869 (internal) and 0.825 (external). Significant net reclassification improvements were also observed (NRI = 1.536 internal, 1.296 external; all P < 0.001). A lumbar MRI-radiomics strategy enables practical, non-invasive risk stratification of nd-MM in resource-constrained environments.