Purpose: To develop a nomogram incorporating B-mode ultrasound (BMUS) and shear-wave elastography (SWE) radiomics to predict malignant status of breast lesions seen on US non-invasively. Methods: Data on 278 consecutive patients from Hospital #1 (training cohort) and 123 cases from Hospital #2 (external validation cohort) referred for breast US with subsequent histopathologic analysis between May 2017 and October 2019 were retrospectively collected. Using their BMUS and SWE images, we built a radiomics nomogram to improve radiology workflow for management of breast lesions. The performance of the algorithm was compared with a consensus of three ACR BI-RADS committee experts and four individual radiologists, all of whom interpreted breast US images in clinical practice. Results: Twelve features from BMUS and three from SWE were selected finally to construct the respective radiomic signature. The nomogram based on the dual-modal US radiomics achieved good diagnostic performance in the training (AUC 0.96; 95% confidence intervals [CI], 0.94-0.98) and the validation set (AUC 0.92; 95% CI, 0.87-0.97). For the 123 test lesions, the algorithm achieved 105 of 123 (85%) accuracy, comparable to the expert consensus (104 of 123 [85%], P = 0.86) and four individual radiologists (93, 99, 95 and 97 of 123, with P value of 0.05, 0.31, 0.10 and 0.18 respectively). Furthermore, the model also performed well in the BIRADS 4 and 5 categories. Conclusions: Performance of a dual-model US radiomics nomogram based on SWE for breast lesion classification may comparable to that of expert radiologists who used ACR BI-RADS guideline.