Published online Jun 28, 2016. doi: 10.4329/wjr.v8.i6.600
Peer-review started: November 6, 2015
First decision: November 29, 2015
Revised: March 1, 2016
Accepted: March 17, 2016
Article in press: March 18, 2016
Published online: June 28, 2016
AIM: To build and evaluate predictive models for contrast-enhanced ultrasound (CEUS) of the breast to distinguish between benign and malignant lesions.
METHODS: A total of 235 breast imaging reporting and data system (BI-RADS) 4 solid breast lesions were imaged via CEUS before core needle biopsy or surgical resection. CEUS results were analyzed on 10 enhancing patterns to evaluate diagnostic performance of three benign and three malignant CEUS models, with pathological results used as the gold standard. A logistic regression model was developed basing on the CEUS results, and then evaluated with receiver operating curve (ROC).
RESULTS: Except in cases of enhanced homogeneity, the rest of the 9 enhancement appearances were statistically significant (P < 0.05). These 9 enhancement patterns were selected in the final step of the logistic regression analysis, with diagnostic sensitivity and specificity of 84.4% and 82.7%, respectively, and the area under the ROC curve of 0.911. Diagnostic sensitivity, specificity, and accuracy of the malignant vs benign CEUS models were 84.38%, 87.77%, 86.38% and 86.46%, 81.29% and 83.40%, respectively.
CONCLUSION: The breast CEUS models can predict risk of malignant breast lesions more accurately, decrease false-positive biopsy, and provide accurate BI-RADS classification.
Core tip: Many studies published show that there are some enhanced patterns such as rapid, hyper-enhancement or enlarged size after contrast may predict malignant, but none of them reliably differentiates malignant from benign nodules. We try to build 6 predictive models (3 malignant and 3 benign) using a qualitative analysis of enhancement patterns, and get diagnostic sensitivity, specificity, and accuracy of the malignant vs benign contrast-enhanced ultrasound (CEUS) models were 84.38%, 87.77%, 86.38% and 86.46%, 81.29% and 83.40%, respectively. It shows that the breast CEUS models can predict risk of malignant breast lesions more accurately, decrease false-positive biopsy, and provide accurate breast imaging reporting and data system classification.