Axillary Nodal Metastases in Italian Early Breast Cancer Patients with Positive Sentinel Lymph Node: Can Axillary Node Dissection be Avoided by using Predictive Nomograms?

Aims and Background Clinical guidelines recommend axillary lymph node dissection (ALND) in cases of metastatic sentinel lymph node (SNL) in patients with clinically node-negative early breast cancer. However, a relevant number of ALND could be avoided in a subset of patients in whom the risk of non-SNL metastases is low. In order to define this population, several authors have proposed mathematical models, which have been validated in many studies. These studies reached different conclusions regarding which model demonstrated the best statistical discrimination power, mainly due to differences in clinical and pathologic variables used, and particularly differences in the number of dissected SLNs. Methods We retrospectively reviewed clinically node-negative patients who underwent ALND in our surgical ward after the diagnosis of breast cancer metastases on SLN biopsy from January 2000 to December 2012. The predictive accuracy of the widely used nomograms to predict the risk of additional nodal disease in our patients with SLN breast cancer metastases was measured by receiver operating characteristic curve. We then attempted to develop a new nomogram by analyzing the dataset. Results A total of 105 patients were included in this study, with ratio of metastatic lymph node/removed lymph node of about 0.89; we found axillary nodal metastases on ALND in only 31 patients (29.5%). Applied to our dataset, Mayo nomogram showed the best area under the receiving operator characteristic curve (0.74) followed by our model (0.71). Instead, the Memorial Sloan-Kettering model showed poor discrimination, as did Tenon (0.56). Conclusions Based on our data, we cannot recommend the clinical use of validated predictive nomograms in order to avoid ALND. We suggest setting up a multicenter Italian study to build a model specific to our setting and based on larger series.