Prediction of non-sentinel lymph node involvement in breast cancer patients with a positive sentinel lymph node.

Completion axillary lymph node dissection (cALND) is the golden standard if breast cancer involves the sentinel lymph node (SLN). However, most non-sentinel lymph nodes (NSLN) are not involved, cALND has a considerable complication rate and does not improve outcome. We here present and validate our predictive model for positive NSLNs in the cALND if the SLN is positive. Consecutive early breast cancer patients from one center undergoing cALND for a positive SLN were included. We assessed demographic and clinicopathological variables for NSLN involvement. Uni- and multivariate analysis was performed. A predictive model was built and validated in two external centers. 21.9% of 470 patients had at least one involved NSLN. In univariate analysis, seven variables were significantly correlated with NSLN involvement: tumor size, grade, lymphovascular invasion (LVI), number of positive and negative SLNs, size of SLN metastasis and intraoperative positive SLN. In multivariate analysis, LVI, number of negative SLNs, size of SLN metastasis and intraoperative positive pathological evaluation were independent predictors for NSLN involvement. The calculated risk resulted in an AUC of 0.76. Applied to the external data, the model was accurate and discriminating for one (AUC = 0.75) and less for the other center (AUC = 0.58). A discriminative predictive model was constructed to calculate the risk of NSLN involvement in case of a positive SLN. External validation of our model reveals differences in performance when applied to data from other institutions concluding that such a predictive model requires validation prior to use.

Is the sentinel lymph node biopsy more sensitive for the identification of positive lymph nodes in breast cancer than the axillary lymph node dissection?

Since the routine clinical use of the sentinel lymph node (SLN) procedure, questions have been raised concerning an increase in the overall percentage of node-positive patients. The goal of our study was to compare the sensitivity of the SLN procedure and the axillary lymph node dissection (ALND) for the identification of positive lymph nodes in breast cancer. The incidence of axillary node metastasis in SLNB and ALND specimens from patients undergoing operative treatment of a primary breast carcinoma was compared retrospectively. Logistic regression models were used to analyze the effect of various predictors on the presence of positive lymph nodes. We constructed a multivariate model including the procedure and these predictors that have shown to be related to lymph node involvement in univariate analysis. The probability of finding positive lymph nodes was thus calculated in both groups correcting for relevant predictors of lymph node involvement. The SLNB group included 830 patients, the ALND group 320. In a multivariate analysis, adjusting for the number of foci, tumor location in the breast, tumor size, LVI, ER, PR, tumor grade and histological subtype, the probability of finding positive lymph nodes was higher with SLNB procedure than with an ALND. However, this difference was not statistically significant (OR 0.7635; CI 0.5334-1.0930, p 0.1404). For comparable tumors, SLNB procedure is at least as sensitive as ALND for detecting positive lymph nodes.

Predictors of axillary lymph node metastases in early breast cancer and their applicability in clinical practice.

PURPOSE: Lymph node involvement is the most important prognostic factor in breast cancer. It is a multifactorial event determined by patient and tumour characteristics. The purpose of this study was to determine clinical and pathological factors predictive for axillary lymph node metastasis (ALNM) in patients with early breast cancer and to build a model to portend lymph node involvement. METHODS: We evaluated 1300 consecutive patients surgically treated in our institution (2007-2009) for cT1-T2 invasive breast cancer. The patient and tumour characteristics evaluated included: age at diagnosis, number of foci, histologic grade, location, tumour size, histologic subtype, lymphovascular invasion (LVI), estrogen-receptor (ER), progesterone-receptor (PR) and Her-2 status. Univariate and multivariate analyses were performed. Factors significantly associated with ALNM by univariate analysis plus histologic subtype were included in the multivariate analysis. RESULTS: By univariate analysis, the incidence of ALNM was significantly associated with the presence of LVI (P < 0.0001), larger tumour size (P < 0.0001), higher histologic grade (P < 0.0001), retroareolar or lateral location in the breast (P < 0.0001), multiple foci (P = 0.0002) and in patients who underwent an axillary lymph node dissection. We found no effect of age, ER/PR nor HER-2 status. By multivariate analysis, ALNM was significantly associated with the presence of LVI (P < 0.0001), larger tumour size (P < 0.0001), axillary lymph node dissection (P = 0.0003), retroareolar and lateral tumour location in the breast (P = 0.0019) and the presence of multiple foci (P = 0.0155). CONCLUSIONS: LVI and tumour size emerged as the most powerful independent predictors of ALNM, followed by the location of the tumour in the breast and the presence of multiple foci.