Background Multiple myeloma (MM) is a malignancy characterized by abnormal plasma cell proliferation. While bortezomib has improved outcomes, significant individual variability persists. Accurate early prediction of patient progression is crucial for optimizing therapeutic intensity and improving long-term survival. Developing an automated, multimodal prediction model can provide clinicians with a robust tool for personalized prognosis, thereby reducing the burden of ineffective treatments on patients. Methods We enrolled 207 newly diagnosed MM (NDMM) patients treated with bortezomib. Based on 2-year outcomes, patients were categorized into progression and non-progression groups. Bone marrow smear images, electrophoresis images, and baseline clinical data were used to train a multimodal ensemble learning model. Neural networks were employed for image feature extraction—ResNet and MobileNet for bone marrow smears; VGG16 and DenseNet for electrophoresis images. Clinical features were selected using LASSO and modeled with Random Forest and Logistic Regression. The best-performing models from each modality were integrated using a soft voting ensemble strategy. Results The ensemble model outperformed all single-modality models (area under the curve (AUC): 0.8180, Accuracy: 0.7000). Among single modalities, electrophoresis image-based models performed best—VGG16 achieved the highest accuracy (AUC: 0.8082, Accuracy: 0.7000), and DenseNet showed the highest AUC (0.8088, Accuracy: 0.6200). ResNet was optimal for bone marrow smears (AUC: 0.7295, Accuracy: 0.5800), while Logistic Regression led clinical data performance (AUC: 0.6779, Accuracy: 0.6800). Conclusion This multimodal ensemble model effectively predicts MM progression by integrating diverse diagnostic data. By enabling earlier identification of high-risk patients, this model serves as a practical decision-support tool for clinicians to tailor personalized treatment strategies.
Li et al. (2026) studied this question.