Objective To explore the diagnostic value of quantitative radiomics features from dual-modal ultrasound composed of elastography and B-mode for axillary lymph node metastasis in breast cancer patients. Methods We retrospectively analyzed 161 axillary lymph nodes (69 benign and 92 metastatic) undergoing real-time elastography and B-mode ultrasound from 158 patients with breast cancer. We extracted a total of 428 features, consisting of morphologic features from B-mode, and intensity features and gray-level co-occurrence matrix features from the dual modalities,and the optimal subsut of features was selected through least absolute shrinkage and selection operator (Lasso) under the condition of leave-one-out cross validation. We used SVM for the classification of benign and metastatic nodes. Results The sensitivity, specificity, accuracy and Youden's index of the 35 radiomics features selected with Lasso were 86.96%, 85.51%, 86.34% and 72.46%, respectively. Conclusion The radiomics features from dual-modal ultrasound (elastography and B-mode) have demonstrated good performance for classification and have potential to be applied to clinical diagnosis of axillary lymph node metastasis.

The purpose of our study is to evaluate the diagnostic performance of radiomics in multi-class discrimination of lymphadenopathy based on elastography and B-mode dual-modal ultrasound images. We retrospectively analyzed a total of 251 lymph nodes (89 benign lymph nodes, 70 lymphoma and 92 metastatic lymph nodes) from 248 patients, which were examined by both elastography and B-mode sonography. Firstly, radiomic features were extracted from multimodal ultrasound images, including shape features, intensity statistics features and gray-level co-occurrence matrix texture features. Secondly, three feature selection methods based on information theory were used on the radiomic features to select different subsets of radiomic features, consisting of conditional infomax feature extraction, conditional mutual information maximization, and double input symmetric relevance. Thirdly, the support vector machine classifier was performed for diagnosis of lymphadenopathy on each radiomic subsets. Finally, we fused the results from different modalities and different radiomic feature subsets with Adaboost to improve the performance of lymph node classification. The results showed that the accuracy and overall 1 score with five-fold cross-validation were 76.09%±1.41% and 75.88%±4.32%, respectively. Moreover, when considering on benign lymph nodes, lymphoma or metastatic lymph nodes respectively, the area under the receiver operating characteristic curve of multi-class classification were 0.77, 0.93 and 0.84, respectively. This study indicates that radiomic features derived from multimodal ultrasound images are benefit for diagnosis of lymphadenopathy. It is expected to be useful in clinical differentiation of lymph node diseases.
Elasticity Imaging Techniques ; Humans ; Lymph Nodes ; Lymphadenopathy ; Retrospective Studies ; Ultrasonography