Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment.

backgroundBreast non-mass-like lesions (NMLs) account for 9.2% of all breast lesions. The specificity of the ultrasound diagnosis of NMLs is low, and it cannot be objectively classified according to the 5

aimTo analyze the features of benign and malignant breast NMLs in grayscale ultrasonography (US), color Doppler flow imaging (CDFI) and CEUS, and to explore the efficacy of the combined diagnosis of NMLs and the effect of CEUS on the BI-RADS classification of NMLs.

methodsA total of 51 breast NMLs verified by pathology were analyzed in our hospital from January 2017 to April 2019. All lesions were examined by US, CDFI and CEUS, and their features from those examinations were analyzed. With pathology as the gold standard, binary logic regression was used to analyze the independent risk factors for malignant breast NMLs, and a regression equation was established to calculate the efficiency of combined diagnosis. Based on the regression equation, the combined diagnostic efficiency of US combined with CEUS (US + CEUS) was determined. The initial BI-RADS-US classification of NMLs was adjusted according to the independent risk factors identified by CEUS, and the diagnostic efficiency of CEUS combined with BI-RADS (CEUS + BI-RADS) was calculated based on the results. ROC curves were drawn to compare the diagnostic values of the three methods, including US, US + CEUS, and CEUS + BI-RADS, for benign and malignant NMLs.

resultsMicrocalcification, enhancement time, enhancement intensity, lesion scope, and peripheral blood vessels were significantly different between benign and malignant NMLs. Among these features, microcalcification, higher enhancement, and lesion scope were identified as independent risk factors for malignant breast NMLs. When US, US + CEUS, and CEUS + BI-RADS were used to identify the benign and malignant breast NMLs, their sensitivity rates were 82.6%, 91.3%, and 87.0%, respectively; their specificity rates were 71.4%, 89.2%, and 92.9%, respectively; their positive predictive values were 70.4%, 87.5%, and 90.9%, respectively; their negative predictive values were 83.3%, 92.6%, and 89.7%, respectively; their accuracy rates were 76.5%, 90.2%, and 90.2%, respectively; and their corresponding areas under ROC curves were 0.752, 0.877 and 0.903, respectively.

conclusionUS combined with CEUS can improve diagnostic efficiency for NMLs. The adjustment of the BI-RADS classification according to the features of contrast-enhanced US of NMLs enables the diagnostic results to be simple and intuitive, facilitates the management of NMLs, and effectively reduces the incidence of unnecessary biopsy.