Abstract: Objective：To assess the diagnostic value of virtual touch tissue imaging quantification(VTIQ) technique in identifying benign and malignant breast lesions with Breast Imaging Reporting and Data System(BI-RADS 4)4 classification on conventional ultrasound, and improving the BI-RADS 4A breast lesions. Methods：Clinical data of 86 cases were analyzed retrospectively, also their BI-RADS 4 and VTIQ features were summarized. The pathology results was the golden standard, the value of VTIQ in the identification of BI-RADS 4 classification were compared. Receiver operating characteristic ( ROC) curve were plotted to determine the most accurate SWV value and the cutoff value for differential diagnosis. The predictive value of VTIQ was compared with the pathological results. Also, 51 BI-RADS 4A lesions were optimized and downgraded according to the mean values of SWV. Results：Of 86 breast lesions, 30 with 34.88% were classified as malignant ones and 56 with 65.12% as benign ones by VTIQ. The maximum value of SWV(SWVmax), the minimum value of SWV(SWVmin) and the mean value of SWV(SWVmean) by VTIQ in malignant lesions were significantly higher than those in the benign ones(P＜0.001),respectively. The SWVmean was the highest efficiency in differentiating benign from malignant lesions, and the cutoff value of SWVmean is 3.58 m/s. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of VTIQ was 83.33%（25/30）, 87.50%（49/56）, 86.05%（74/86）, 78.13%（25/32）and 90.74%（49/54）, respectively. According to the evaluation of SWVmean with 3.58 m/s, 46 cases among 51 cases about 90.20% of BI-RADS 4A class can be adjusted from 4A class down to 3 class because of less than the cutoff value. But there was one case of malignant tumor is misclassified as BI-RADS 4 3 class, increasing the false negative rate. Conclusions：VTIQ technology combined with BI-RADS 4 classification is helpful to differentiate diagnosis of benign and malignant breast masses, especially can improve the BI-RADS 4A type of tumor classification by reducing unnecessary biopsy or surgery in most 4 a class lesions.