Background Multiple predictive systems have previously been developed to identify the sentinel lymph node (SLN)-positive patients at low risk of additional axillary non-SLN involvement and for whom completion axillary lymph node dissection (ALND) could be avoided. However, previous studies showed that these tools had poor performance in Dutch patients with breast cancer, probably owing to variations in pathology settings and differences in population characteristics. The aim of the present study was to develop a predictive tool for the risk of non-SLN involvement in a Dutch population with SLN-positive breast cancer. Materials and Methods The data from 513 patients with SLN-positive breast cancer at 10 participating hospitals, who had undergone ALND from January 2007 to December 2008 were studied. The uni- and multivariable associations of predictors for non-SLN metastases were analyzed, and a predictive model was developed. The discriminatory ability of the model was measured by the area under the receiver operating characteristic curve (AUC) and the agreement between predicted probabilities and observed frequencies was visualized by a calibration plot. Results A predictive model was developed that included the 2 strongest predictors: the size of the SLN metastases in millimeters and the presence of a negative sentinel lymph node. The model showed good discriminative ability (AUC, 0.75) and good calibration over the complete range of predicted probabilities. Conclusion We have developed a tool to predict additional non-SLN metastases in Dutch patients with SLN-positive breast cancer that is easy to use in daily clinical breast cancer practice.

Axillary lymph node involvement, Non-SLN metastases, Predictive system, SLN-positive breast cancer,
Clinical Breast Cancer
Department of Public Health

Van Den Hoven, I, van Klaveren, D, Voogd, A.C, Vergouwe, Y, Tjan-Heijnen, V.C.G, & Roumen, R.M.H. (2016). A Dutch Prediction Tool to Assess the Risk of Additional Axillary Non-Sentinel Lymph Node Involvement in Sentinel Node-Positive Breast Cancer Patients. Clinical Breast Cancer, 16(2), 123–130. doi:10.1016/j.clbc.2015.09.003