Models for predicting non-sentinel lymph node positivity in sentinel node positive breast cancer: the importance of scoring system.

BACKGROUND: Although delayed axillary lymph node dissection is the gold standard for evaluating axillary status after identification of a positive sentinel lymph node (SLN), between 40% and 70% of sentinel lymph node positive patients will have negative non-sentinel nodes and undergo a non-therapeutic axillary dissection. Accurate estimates of the likelihood of additional disease in the axilla can assist decision-making about further treatment. To predict non-SLN metastases in patients with a positive SLN biopsy, four different nomograms have been created. METHOD: This paper reviews the scoring systems and nomograms reported in the literature and compares their predictive probability of non-SLN involvement in patients with SLN positive breast cancer. RESULT: There are several published scoring systems that contain different parameters to estimate the rate of non-SLN metastases in SLN positive patients. We reviewed Memorial Sloan-Kettering Cancer Center (MSKCC), Tenon, Stanford and Cambridge nomograms published and used scoring systems including three to eight variables. We found that the MSKCC nomogram is the most validated model in the literature to predict non-SLN status accurately. The other three models have not yet been verified in outside institutions. CONCLUSION: Despite having some limitations, the MSKCC nomogram is the most validated model in the literature. These models should be tested and verified in different programs and different patient groups before they are widely accepted.

Validation of breast cancer nomograms for predicting the non-sentinel lymph node metastases after a positive sentinel lymph node biopsy in a multi-center study.

OBJECTIVE: In the study, our aim was to evaluate the predictability of four different nomograms on non-sentinel lymph node metastases (NSLNM) in breast cancer (BC) patients with positive sentinel lymph node (SLN) biopsy in a multi-center study. METHODS: We identified 607 patients who had a positive SLN biopsy and completion axillary lymph node dissection (CALND) at seven different BC treatment centers in Turkey. The BC nomograms developed by the Memorial Sloan Kettering Cancer Center (MSKCC), Tenon Hospital, Cambridge University, and Stanford University were used to calculate the probability of NSLNM. Area under (AUC) Receiver Operating Characteristics Curve (ROC) was calculated for each nomogram and values greater than 0.70 were accepted as demonstrating good discrimination. RESULTS: Two hundred and eighty-seven patients (287) of 607 patients (47.2%) had a positive axillary NSLNM. The AUC values were 0.705, 0.711, 0.730, and 0.582 for the MSKCC, Cambridge, Stanford, and Tenon models, respectively. On the multivariate analysis; overall metastasis size (OMS), lymphovascular invasion (LVI), and proportion of positive SLN to total SLN were found statistically significant. We created a formula to predict the NSLNM in our patient population and the AUC value of this formula was 0.8023. CONCLUSIONS: The MSKCC, Cambridge, and Stanford nomograms were good discriminators of NSLNM in SLN positive BC patients in this study. A newly created formula in this study needs to be validated in prospective studies in different patient populations. A nomogram to predict NSLNM in patients with positive SLN biopsy developed at one institution should be used with caution.