Radiomics Nomogram of DCE-MRI for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer - Mao, Ning; Dai, Yi; Lin, Fan; Ma, Heng; Duan, Shaofeng; Xie, Haizhu; Zhao, Wenlei; Hong, Nan (Frontiers in Oncology, 2020)
Purpose: This study aimed to establish and validate a radiomics nomogram based on dynamic contrast-enhanced (DCE)-MRI for predicting axillary lymph node (ALN) metastasis in breast cancer. Method: This retrospective study included 296 patients with breast cancer who underwent DCE-MRI examinations between July 2017 and June 2018. A total of 396 radiomics features were extracted from primary tumor. In addition, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the features. The radiomic signature and independent risk factors were incorporated to build a radiomics nomogram model. The calibration curve and receiver operator characteristic (ROC) curve were used to confirm the performance of the nomogram in the training and validation sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA). Results: The radiomics signature consisted of five ALN-status-related features, and the nomogram model included the radiomics signature and the MR-reported lymph node (LN) status. The model showed good calibration and discrimination, with areas under the ROC curve (AUC) of 0.92 [95% confidence interval (CI), 0.87–0.97]) in the training set and 0.90 (95% CI, 0.85–0.95) in the validation set. In the MR-reported LN-negative (cN0) subgroup, the nomogram model also exhibited favorable discriminatory ability (AUC, 0.79; 95% CI, 0.70–0.87). DCA indicated that the nomogram model was clinically useful. Conclusions: The MRI-based radiomics nomogram model could be used to predict the ALN metastasis of breast cancer preoperatively.