Prediction of Non-sentinel Lymph Node Metastases After Positive Sentinel Lymph Nodes Using Nomograms

Only 30-50% of patients with sentinel lymph node (SLN) metastases present with further axillary lymph node metastases. Therefore, up to 70% of patients with positive SLN are overtreated by axillary dissection (AD) and may suffer from complications such as sensory disturbances or lymphedema. According to the current S3 guidelines, AD can be avoided in patients with a T1/T2 tumor if breast-conserving surgery with subsequent tangential irradiation is performed and no more than two SLNs are affected. Additionally, use of nomograms, that predict the probability of non-sentinel lymph node (NSLN) metastases, is recommended. Therefore, models for the prediction of NSLN metastases in our defined population were constructed and compared with the published nomograms.

In a retrospective study, 2,146 primary breast cancer patients, who underwent SLN biopsy at the University Women's Hospital in Tuebingen, were evaluated by dividing the patient group in a training and validation collective (TC or VC). Using the SLN-positive TC patients, three models for the prediction of the likelihood of NSLN metastases were adapted and were then validated using the SLN-positive VC patients. In addition, the predictive power of nomograms from Memorial Sloan Kettering Cancer Center (MSKCC), Stanford, and the Cambridge model were compared with regard to our patient collective.

A total of 2,146 patients were included in the study. Of these, 470 patients had positive SLN, 295 consisted the training collective and 175 consisted the validation collective. In a regression model, three variants - with 11, 6 and 2 variables - were developed for the prediction of NSLN metastases in our defined population and compared to the most frequently used nomograms. Our variants with 11 and with 6 variables were proven to be a particularly suitable model and showed similarly good results as the published MSKCC nomogram.

Our developed nomograms may be used as a prediction tool for NSLN metastases after positive SLN.