QUANTIFICATION OF ELASTIC HETEROGENEITY USING CONTOURLET-BASED TEXTURE ANALYSIS IN SHEAR-WAVE ELASTOGRAPHY FOR BREAST TUMOR CLASSIFICATION

Ultrasound shear-wave elastography (SWE) has become a valuable tool for diagnosis of breast tumors. The purpose of this study was to quantify the elastic heterogeneity of breast tumors in SWE by using contourlet-based texture features and evaluating their diagnostic performance for classification of benign and malignant breast tumors, with pathologic results as the gold standard. A total of 161 breast tumors in 125 women who underwent B-mode and SWE ultrasonography before biopsy were included. Five quantitative texture features in SWE images were extracted from the directional subbands after the contourlet transform, including the mean (T-mean), maximum (T-max), median (T-med), third quartile ((Tqt)), and standard deviation (T-sd) of the subbands. Diagnostic performance of the texture features and the classic features was compared using the area under the receiver operating characteristic curve (AUC) and the leave-one-out cross validation with Fisher classifier. The feature Tmean achieved the highest AUC (0.968) among all features and it yielded a sensitivity of 89.1%, a specificity of 94.3% and an accuracy of 92.5% for differentiation between benign and malignant tumors via the leave-one-out cross validation. Compared with the best classic feature, i.e., the maximum elasticity, T-mean improved the AUC, sensitivity, specificity and accuracy by 3.5%, 12.7%, 2.8% and 6.2%, respectively. The T-med, T-qt and T-sd were also superior to the classic features in terms of the AUC and accuracy. The results demonstrated that the contourlet-based texture features captured the tumor's elastic heterogeneity and improved diagnostic performance contrasted with the classic features. (C) 2015 World Federation for Ultrasound in Medicine & Biology.