Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: From static images to CEUS video analysis - Zhu, Jun-Yan; He, Han-Lu; Lin, Zi-Mei; Zhao, Jian-Qiang; Jiang, Xiao-Chun; Liang, Zhe-Hao; Huang, Xiao-Ping; Bao, Hai-Wei; Huang, Pin-Tong; Chen, Fen (Frontiers in Oncology, 2022)
Background Continuous contrast-enhanced ultrasound (CEUS) video is a challenging direction for radiomics research. We aimed to evaluate machine learning (ML) approaches with a radiomics combined with XGBoost model and a convolutional neural network (CNN) for discriminating between benign and malignant lesions in CEUS videos with a duration of more than one minute. Methods We gathered the breast CEUS videos of 109 benign and 81 malignant tumors from 2 centers. A radiomics combined with XGBoost model and a CNN were used to classify the breast lesions on the CEUS videos. The lesions were manually segmented by one radiologist. The radiomics combined with XGBoost model was conducted with a variety of data sampling methods. The CNN used pretrained 3D residual network (ResNet) models with 18, 34, 50 and 101 layers. The machine interpretations were compared with prospective interpretations by two radiologists. Breast biopsies or pathological examinations were used as the reference standard. Areas under the receiver operating curves (AUCs) were used to compare the diagnostic performance of the models. Results The CNN model achieved the best AUC of 0.84 on the test cohort with the 3D-ResNet-50 model. The radiomics model obtained AUCs between 0.65-0.75. Radiologists 1 and 2 had AUCs of 0.75 and 0.70, respectively. Conclusions The 3D-ResNet-50 model was superior to the radiomics combined with XGBoost model in classifying enhanced lesions as benign or malignant on CEUS videos. The CNN model was superior to the radiologists, and the radiomics model performance was close to the performance of the radiologists.