Application of S-Detect classification system in diagnosis of breast benign and malignant mass by ultrasound

Abstract

Objective To investigate the value of S-Detect classification in differential diagnosis of breast mass. Methods The data of forty-seven patients with breast mass lesions (n=61) from our hospital during January to December in 2016 were retrospectively analyzed. Both the man-made BI-RADS classification (identified by three different specialist physicians with 2, 5 and 7 years of experience, respectively) and computer S-Detect classification were performed. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the man-made BI-RADS classification and S-Detect classification of the benign or malignant diagnosis of breast lumps were calculated. The ROC curve was further plotted, and the area under the curve (AUC) of each group was compared, respectively. Results Sixty-one breast mass lesions were confirmed 36 benign lesions and 25 malignant lesions by pathological biopsy. The sensitivity, specificity and accuracy of man-made BI-RADS classification were as follows: 2-year experience physicians 69.4%, 72.0% and 70.5%; 5-year experience physicians: 64.0%, 92.0% and 75.4%; 7-year experience physicians: 69.4%, 92.0% and 78.7%. The diagnostic sensitivity, specificity, and accuracy of S-Detect classification were 80.6%, 96.0% and 86.9%. The specificity, accuracy and positive predictive value of S-Detect classification were significantly higher than those of 2-year experience physicians by BI-RADS classification (P<0.05). The area under the ROC curve of each group was 0.729, 0.786 and 0.801 for 2, 5 and 7-year experience physicians, respectively, and 0.917 for S-Detect classification. Conclusions Compared with the man-made BI-RADS classification, S-Detect classification has advantages in diagnosis of the benign or malignant of breast mass and is helpful to improve the accuracy of diagnosis, especially for junior physicians. Key words: Ultrasonography, mammary; S-Detect classification; Breast diseases; Man-made BI-RADS classification