Diagnostic value of virtual touch tissue imaging quantification combined with BI-RADS classification in breast lesions

Abstract

Objective To assess the diagnostic value of virtual touch imaging quantification (VTIQ) technique combined with breast imaging reporting and data system (BI-RADS) classification for benign and malignant breast lesions. Methods A total of 172 breast lesions were selected from 172 patients who were admitted to the First Affiliated Hospital of Harbin Medical University from August 2016 to April 2017, and all the lesions were first examined by US and classified by BI-RADS, and then examined by elastograpy. Using the VTIQ mode, we can obtain both the quality mode map and the speed mode map, and record seven sets of SWV values for each lesion to calculate the average. Using the pathological results as the gold standard, the receiver operating characteristic (ROC) curves of VTIQ, BI-RADS classification method, and VTIQ combined with BI-RADS classification method in the identification of benign and malignant breast lesions were plotted to calculate the area under the curve and determine the cut-off value. The diagnostic efficacy of the three methods was compared. The biopsy rates of breast lesions calculated by BI-RADS classification and VTIQ combined with BI-RADS classification were also compared and analyzed. Results The area under the ROC curve of the BI-RADS classification was 0.762, and the best diagnostic boundary value was between BI-RADS 3 and 4a classification. The area under the ROC curve of VTIQ (SWV average) was 0.895, and the optimal diagnostic cut-off value was 3.13 m/s. The area under the ROC curve of the VTIQ combined with the BI-RADS classification was 0.908, which was significantly higher than that of the BI-RADS classification (Z=5.79, P<0.01). According to the ROC curve, the best diagnostic boundary value of VTIQ combined with BI-RADS classification was between BI-RADS 4a and 4b, and the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92.9%, 88.8%, 80.0%, 96.3%, and 90.1%, respectively. Compared with the BI-RADS classification, the diagnostic specificity, accuracy, and positive predictive value of the VTIQ combined with the BI-RADS classification increased by 32.8%, 20.9%, and 28.6%, respectively. According to the BI-RADS classification, there were 51 cases requiring biopsy among the 116 benign lesions (51/116, 43.9%), and there were only 16 patients requiring further biopsy according to the VTIQ combined with BI-RADS classification (16/116, 13.8%). There was a significant difference in the biopsy rate between them (χ2=25.71, P=0.00). Conclusion VTIQ technology combined with BI-RADS classification shows good diagnostic performance for benign and malignant breast lesions. Compared with BI-RADS classification alone, VTIQ technology combined with BI-RADS classification can improve the specificity and accuracy of diagnosis and reduce the clinical biopsy rate of benign lesions and unnecessary clinical interventions. Elasticity imaging technique can be used as an effective supplement and an auxiliary diagnostic method for the conventional ultrasound BI-RADS classification. Key words: Breast disease; Diagnosis, Differential; Ultrasonography; Breast imaging reporting and data system; Elasticity imaging techniques