Jingfeng Suo1, Qi Zhang1, Wanying Chang2, Jun Shi1, Zhuangzhi Yan1, Man Chen2. 1. Institute of Biomedical Engineering, Shanghai University, Shanghai, 200444. 2. Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025.
Abstract
OBJECTIVES: 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. CONCLUSIONS: 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.
OBJECTIVES: 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 cancerpatients. 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. CONCLUSIONS: 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.
Entities:
Keywords:
axillary lymph node; breast cancer; multimode; radiomics; real-time elastography