A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases

Background. We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients. Methods. We enrolled 209 patients with breast cancer who underwent SLN biopsy and axillary lymph node dissection. We evaluated the relationships between non-SLN metastases and clinicopathologic features, as well as preoperative routine tests of blood indexes, tumor markers, and serum lipids, including lipoprotein a (Lp(a)). Risk factors for non-SLN metastases were identified by logistic regression analysis. The nomogram was created using the R program to predict the risk of non-SLN metastases in the training set. Receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in the validation set. Results. Lp(a) was significantly associated with non-SLN metastasis status. Compared with the MSKCC model, the predictive ability of our new nomogram that combined Lp(a) level and clinical variables (pathologic tumor size, lymphovascular invasion, multifocality, and positive/negative SLN numbers) was significantly greater (AUC: 0.732, 95% CI: 0.643–0.821) (C-index: 0.703, 95% CI: 0.656–0.791) in the training cohorts and also performed well in the validation cohorts (C-index: 0.773, 95% CI: 0.681–0.865). Moreover, the new nomogram with Lp(a) improved the accuracy (12.10%) of identification of patients with non-SLN metastases (NRI: 0.121; 95% CI: 0.081–0.202; P = 0.011 ). Conclusions. This novel nomogram based on preoperative serum indexes combined with clinicopathologic features facilitates accurate prediction of risk of non-SLN metastases in Chinese patients with breast cancer.