Objective To develop a nomogram model based on radiomics，clinical and shear wave elastography（SWE）, and to explore the efficiency of the model in differentiating benign and malignant lesions in BI-RADS 4 category lesions. Methods From December 2017 to June 2023, 403 patients with BI-RADS 4 category lesions from 3 hospitals were retrospectively reported. The clinical characteristics，SWE parameters and pathological results were collected. A total of 283 breast lesions in Nanjing Drum Tower Hospital and the First Affiliated Hospital of Anhui Medical University from December 2017 to December 2019 were divided into training set. A total of 120 breast lesions in Beijing Shijitan Hospital from April 2022 to June 2023 were divided into the validation set. According to pathological results, the training set and the validation set were divided into benign and malignant group. The radiomics features of B-mode ultrasound of the lesions were extracted and the radiomics score（Rad-score） was calculated. Univariate and multivariate logistic regression analysis were used to determine the independent predictors of breast cancer and construct a nomogram. The receiver operating characteristic curve，calibration curve and clinical decision curve were used to evaluate the diagnostic efficacy of the nomogram. Results After feature extraction and screening, 13 radiomics features were finally included in the calculation of Rad-score. The Rad-score of the benign and malignant groups were [-1.07 (-1.64, -0.37) points and 0.07 (-0.3, 0.56), respectively], and the difference between them was statistically significant (Z=514,P＜0.001). Multivariate Logistic regression analysis showed that age (OR: 1.107, P＜0.001), maximum shear wave velocity (SWVmax) (OR: 3.919, P＜0.001) and Rad-score (OR: 4.18, P＜0.001) were independent factors in predicting malignant breast lesions. The nomogram model was conducted with age，SWVmax and Rad-score. The area under ROC curve of the nomogram model was higher than that of SWVmax and Rad-score (both P＜0.001) in the training set and validation set, and the fit was good (both P＞0.05). In the validation set, the nomogram model could achieve higher clinical benefits to predict BI-RADS 4 category lesions and could reduce the non-necessary biopsy rate of BI-RADS 4 category lesions by 61.2%. Conclusion The nomogram model based on age, SWVmax and Rad-score can effectively predict the benign and malignant lesions of BI-RADS 4 category lesions, and reduce the rate of unnecessary biopsy.