To identify the contrast-enhanced ultrasound (CEUS) features of the breast, to build a CEUS predictive model for breast lesions, and to evaluate the diagnostic value of this model in distinguishing breast benign from malignant lesions.

A total of 192 patients with 195 breast lesions were included in the study. The lesions were divided into two groups: 123 lesions in a CEUS group and 72 in a CEUS+ dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) group. In the CEUS group, CEUS were used to examine each lesion. Then, risky CEUS patterns in breast malignant lesions were identified by logistic regression analysis to build a breast CEUS predictive model. Using final pathology results as the gold standard, the diagnostic efficiencies of the CEUS prediction model and DCE-MRI were evaluated.

Three independent variables, namely, increased lesion scope (OR=12.941), ″crab foot″ sign (OR=7.553), and filling defect (OR=5.670), were selected in the final step of the logistic regression analysis in the CEUS group. The CEUS predictive model was built as Y=-4.108+ 2.560X₆+ 2.022X₇+ 1.735X₈. Using final pathology results as the gold standard, the area under ROC curve of the CEUS predictive model in distinguishing between benign and malignant breast lesions was calculated to be 0.953, and the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the CEUS predictive model were 93.0%, 73.3%, 93.0%, 73.3%, and 88.9%, respectively; the corresponding values of DCE-MRI were 94.7%, 73.3%, 93.1%, 78.6%, and 90.3%. The consistency between the CEUS risk prediction model and DCE-MRI in the diagnosis of benign and malignant breast lesions was high (Kappa value=0.70).

The breast CEUS predictive model built here can predict the malignant risk of breast lesions more accurately. It is an effective and reliable method for the diagnosis of benign and malignant breast lesions because of its simple operation, short examination time, reproducibility, and relatively low price.