Objective To explore the clinical application value of a nomogram model constructed based on molybdenum target imaging features combined with clinical factors in differentiating benign and malignant BI-RADS type 4A breast masses.

Methods A retrospective analysis was conducted on the clinical and X-ray imaging data of 180 patients with breast masses diagnosed as BI-RADS 4A by mammography from February 2019 to July 2025. Taking the pathological results as the gold standard, there were 92 benign masses and 88 malignant ones. The masses were randomly divided into the training group (n = 125) and validation group (n = 55) at a ratio of 7:3 by the random number table method. The independent predictors for differentiating benign and malignant conditions were screened through univariate and multivariate logistic regression analyses, and a nomogram model was constructed. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve.

Results The results of multivariate logistic regression analysis showed that the age in quantitative continuous data, and whether the edge of the mass was smooth (yes/no) and whether there were concomitant signs around (yes/no) in qualitative data were the independent predictors for differentiating benign and malignant BI-RADS type 4A breast masses. The areas under the ROC curves of the nomogram model constructed based on the above factors in the training group and the validation group were 0.820 (95%CI: 0.747–0.893) and 0.816 (95%CI: 0.700–0.932), respectively. The sensitivity and specificity were 93.2%, 60.6% and 89.7%, 57.7%, respectively. The calibration curve shows that the predicted probability of the model was in good consistency with the actual value. The clinical decision curve showed that the model had good clinical practicability.

Conclusions The nomogram model constructed based on the mammography imaging features and clinical factors of the breast can provide effective individualized malignant risk assessment for BI-RADS type 4A breast masses, and contribute to optimizing clinical decision-making.