Breast tumor diagnosis using radiofrequency signals based ultrasound multifeature maps combined with radiomics analysis

Abstract

Breast cancer is a high incidence of malignancy in women, with a higher mortality rate. Accurate screening is helpful to early detection and improve the treatment success rate and patient survival rate. This study is based on low-cost ultrasound, using ultrasound multifeature maps based on the original radiofrequency (RF) signals and radiomics analysis method to evaluate the benign and malignant of breast tumors. The three ultrasound multifeature maps of breast tumor are composed of direct energy attenuation coefficient (AC), standard deviation of image intensity (SD) and Rician distribution parameters (RD). From the above multifeature maps, high-throughput radiomics features were extracted, then sparse representation method was used for feature selection, and then support vector machine was used to predict the benign and malignant of breast tumors. Eight groups of comparative experiments were established by using ultrasound gray-scale image, single ultrasound feature map and two ultrasound feature maps. The results from 164 patients with breast tumor showed that the AUC, accuracy and sensitivity of the radiomics classification model with feature maps of AC, SD and RD can reach 93.61%, 93.94% and 100%, respectively. The use of RF based ultrasound multifeature maps combined with radiomics could effectively predict the benign and malignant of breast tumors in this study.