A Radiomics Model for Preoperative Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Dynamic Contrast-Enhanced MRI - Ma, Mingming; Jiang, Yuan; Qin, Naishan; Zhang, Xiaodong; Zhang, Yaofeng; Wang, Xiangpeng; Wang, Xiaoying (Frontiers in Oncology, 2022)
Purpose: To develop a radiomics model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify sentinel lymph nodes (SLN) metastasis in breast cancer (BC) patients. Materials and Methods: The MRI images and clinicopathological data of 142 female primary BC patients from January 2017 to December 2018 were included in this study. The patients were randomly divided into the training and testing cohort at a ratio of 7:3. We built four types of radiomics models: (1) a radiomics model based on the ROI of breast tumor; (2) a radiomics model based on the ROI of intra- and peri- breast tumor;(3) a radiomics model based on the ROI of axillary lymph node (ALN) ;(4) a radiomics model based on the ROI of ALN and breast tumor. Receiver operating characteristic curve analysis (ROC) and Decision curve analysis (DCA) were used to assess the performance of the four radiomic models. The technique for order preference by similarity to ideal solution (TOPSIS) through decision matrix analysis was used to select the best model. Results: Models 1, 2, 3, and 4 yielded AUCs of 0.977, 0.999, 0.882 and 1.000 in the training set and 0.699, 0.817, 0.906 and 0.696 in the testing set, respectively, in terms of predicting SLN metastasis. Model 3 had the highest AUC in the testing cohort, only the difference from Model 1 was statistically significant (p=0.022). Decision curve analysis showed that model 3 yielded a greater net benefit to predict SLN metastasis than the other three models in the testing cohort. The best model analyzed by TOPSIS was Model 3. Conclusion: ALNs radiomics features on DCE-MRI is a potential method to evaluate SLN status in BC patients.