MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer - Qiu, Ya; Zhang, Xiang; Wu, Zhiyuan; Wu, Shiji; Yang, Zehong; Wang, Dongye; Le, Hongbo; Mao, Jiaji; Dai, Guochao; Tian, Xuwei; Zhou, Renbing; Huang, Jiayi; et al. (Frontiers in Oncology, 2022)
Background: Overtreatment of axillary lymph node dissection (ALND) may occur in patients with axillary positive sentinel lymph node (SLN) but negative non-SLN (NSLN). Developing a magnetic resonance imaging (MRI)-based radiomics nomogram to predict axillary NSLN metastasis in patients with SLN-positive breast cancer could effectively decrease the probability of overtreatment and optimize a personalized axillary surgical strategy. Methods: This retrospective study included 285 patients with positive SLN breast cancer. 55 of them had metastatic NSLNs and 230 had non-metastatic NSLNs. MRI-based radiomic features of primary tumors were extracted and MRI morphologic findings of the primary tumor and axillary lymph nodes were assessed. Three models including a radiomics signature, an MRI-clinical nomogram, and an MRI-clinical-radiomics nomogram were established based on MRI morphologic findings, clinicopathologic characteristics, and MRI-based radiomic features to predict the NSLN status. The optimal predictors in each model were selected using the 5-fold cross-validation (CV) method. Their predictive performances were determined by the receiver operating characteristic (ROC) curves analysis. The area under the curves (AUCs) of different models were compared by the Delong test. Their discrimination capability, calibration curve, and clinical usefulness were also assessed. Results: The 5-fold CV analysis showed that the AUCs ranged from 0.770 to 0.847 for the radiomics signature, from 0.720 to 0.824 for the MRI-clinical nomogram, from 0.843 to 0.932 for the MRI-clinical-radiomics nomogram. The optimal predictive factors in the radiomics signature, MRI-clinical nomogram, and MRI-clinical-radiomics nomogram were one texture feature of diffusion-weighted imaging (DWI), two clinicopathologic features together with one MRI morphologic finding, and the DWI-based texture feature together with the two clinicopathologic features plus the one MRI morphologic finding, respectively. The MRI-clinical-radiomics nomogram achieved the highest AUC compared with the radiomics signature (0.868 vs. 0.806, P < 0.001) and MRI-clinical nomogram (0.868 vs. 0.761; P < 0.001). The MRI-clinical-radiomics nomogram showed good discrimination and good calibration. Decision curve analysis demonstrated that the MRI-clinical-radiomics nomogram was clinically useful. Conclusion: The MRI-clinical-radiomics nomogram developed in our study showed high predictive performance, which can be used to predict the axillary NSLN status in SLN-positive breast cancer patients before surgery.