Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer

作者：Hongna Tan, Yaping Wu, Fengchang Bao, Jing Zhou, Jianzhong Wan, Jie Tian, Yusong Lin, Meiyun Wang · 发表于：British Journal of Radiology · 年份：2020 · DOI：10.1259/bjr.20191019 · 被引用次数：43 · 研究领域：Breast Cancer Treatment Studies、Digital Radiography and Breast Imaging、Radiomics and Machine Learning in Medical Imaging

OBJECTIVE: To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer. METHODS: = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts. RESULTS: 95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591-0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 an...