Predicting Non-sentinel Lymph Node Metastasis in a Chinese Breast Cancer Population with 1-2 Positive Sentinel Nodes: Development and Assessment of a New Predictive Nomogram.

Abstract

We have developed a new nomogram to predict the probability of a patient with 1-2 metastatic sentinel lymph nodes (SLNs) to present further axillary disease. Data were collected from 480 patients who were diagnosed with 1-2 positive lymph nodes and thus underwent axillary lymph node dissection between March 2005 and June 2011. Clinical and pathological features of the patients were assessed with multivariable logistic regression. The Shanghai Cancer Center Non-SLN nomogram (SCC-NSLN) was created from the logistic regression model. This new model was subsequently applied to 481 patients from July 2011 to December 2013. The predictive accuracy of the SCC-NSLN nomogram was measured by calculating the area under the receiver operating characteristic curve (AUC). Based on the results of the univariate analysis, the variables that were significantly associated with the incidence of non-SLN metastasis in an SLN-positive patient included lymphovascular invasion, neural invasion, the number of positive SLNs, the number of negative SLNs, and the size of SLN metastasis (P < 0.05). Using multivariate analysis, lymphovascular invasion, the number of positive SLNs, the number of negative SLNs, and the size of SLN metastasis were identified as independent predictors of non-SLN metastasis. The SCC-NSLN nomogram was then developed using these four variables. The new model was accurate and discriminating on both the modeling and validation groups (AUC: 0.7788 vs 0.7953). The false-negative rates of the SCC-NSLN nomogram were 3.54 and 9.29 % for the predicted probability cut-off points of 10 and 15 % when applied to patients who have 1-2 positive SLNs. The SCC-NSLN nomogram could serve as an acceptable clinical tool in clinical discussions with patients. The omission of ALND might be possible if the probability of non-SLN involvement is <10 and <15 % in accordance with the acceptable risk determined by medical staff and patients.