Objective To evaluate the diagnostic performance of a combined model incorporating ultrasound video-based radiomics features and clinical variables for distinguishing between benign and malignant breast lesions. Methods A total of 346 patients (173 benign and 173 malignant) were retrospectively enrolled. Breast ultrasound videos were acquired and processed using semi-automatic segmentation in 3D Slicer. Radiomics features were extracted from volumetric tumor regions and refined using feature selection methods. Models were constructed using clinical variables, radiomics features, and their combination. Model performance was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC) values. Results The clinical model incorporating age, tumor size, and Breast Imaging Reporting and Data System (BI-RADS) classification achieved an AUC of 0.873. The radiomics model, utilizing 14 selected features, attained an AUC of 0.836. The combined model, integrating radiomics and clinical data, demonstrated significantly improved predictive performance with an AUC of 0.926, surpassing the BI-RADS-based model (AUC = 0.737). Internal validation using bootstrap resampling confirmed the robustness of the combined model (AUC = 0.901-0.954). Conclusion The integration of ultrasound video-based radiomics with clinical characteristics significantly improves the differentiation of benign and malignant breast tumors compared to conventional BI-RADS evaluation. This approach may enhance diagnostic accuracy and facilitate more precise clinical decision-making.
Wu et al. (Sun,) studied this question.