Background: Women with breast cancer and a positive axillary sentinel lymph node (SLN) are recommended to undergo complete axillary lymph node dissection; however, further nodal disease is not always present. Mathematical models have been constructed to determine the risk of metastatic disease; three of these were evaluated independently.Methods: Data from 108 women with breast cancer who had a positive SLN biopsy and completion axillary lymph node dissection were used. Measurements of additional parameters over those usually determined (such as size of SLN metastasis) were assessed under the supervision of two pathologists. These data were used to determine the predicted risk of non-SLN metastases using three mathematical models (from Memorial Sloan-Kettering Cancer Center (MSKCC), Cambridge University and Stanford University) and a comparison made with the observed findings. Analyses were made using the area under the receiver operating characteristic (ROC) curve (AUC).Results: Some 53 (49.1 per cent) of 108 patients had a positive non-sentinel axillary lymph node metastasis. The AUC values were 0.63, 0.72 and 0.67 for the MSKCC, Cambridge and Stanford nomograms respectively.Conclusion: This independent comparison found no significant difference between the models, although the Cambridge model had the advantage of requiring fewer measurements with a more accurate predictive performance.

Comparison of three mathematical models for predicting the risk of additional axillary nodal metastases after positive sentinel lymph node biopsy in early breast cancer

BACKGROUND:

Several models for the prediction of nonsentinel lymph node (NSLN) metastasis in sentinel lymph node (SLN)-positive breast cancer patients have been proposed. In this study, the authors evaluate the Stanford Online Calculator (SOC), which was designed to predict the likelihood of NSLN metastasis using only 3 variables: primary tumor size, SLN metastasis size, and angiolymphatic invasion status. They compared it with the Mayo and Memorial Sloan-Kettering Cancer Center (MSKCC) nomograms.



METHODS:

The SOC was used to calculate the probability of NSLN metastasis in 464 breast cancer patients with SLN metastasis who underwent completion axillary lymph node dissection at the Mayo Clinic. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Mean probabilities of patients with and without NSLN metastasis were compared. Patients with ≤5%, ≤10%, and 100% NSLN metastasis probabilities were examined.



RESULTS:

The AUCs of the Stanford, MSKCC, and Mayo models were 0.72, 0.74, and 0.77, respectively (P = .13). The mean Stanford probabilities for patients with and without NSLN metastasis were 0.75 (range, 0.06-1.0) and 0.50 (range, 0.05-1.0), respectively (P < .0001). The false-negative rates for patients with a Stanford probability of ≤5% and ≤10% were 0% and 13%, respectively. Of the patients with a Stanford probability of 100%, 26% did not have NSLN metastasis.



CONCLUSIONS:

Despite using only 3 variables, the Stanford nomogram appears to perform on a par with, but not better than, the MSKCC and Mayo nomograms. Further validation in other patient populations is needed. Cancer 2009. © 2009 American Cancer Society.

https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cncr.24469

Assessment of the performance of the Stanford Online Calculator for the prediction of nonsentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients.

Purpose: The markers of prognosis are used to predict the clinical course of disease and the outcome for patients with invasive breast cancer. Our aim is to investigate the relationship of peritumo...

Lymphovascular Invasion, as a Prognostic Marker in Patients with Invasive Breast Cancer

Crosstalk events in the estrogen signaling pathway may affect tamoxifen efficacy in breast cancer molecular subtypes

The value of complete axillary lymph node dissection (ALND) has been questioned in invasive breast cancer (IBC) patients with positive sentinel lymph nodes (SLNs) who have no non-sentinel lymph node (NSLN) metastases. Because biological markers have not been systematically studied in this setting, we sought to identify clinicopathological characteristics and biological markers for predicting NSLN metastases in SLN-positive IBC patients. Two hundred and five IBC patients who had at least one positive SLN and received SLN biopsy and ALND were included in our study. We examined the clinicopathological characteristics of their primary tumors, SLNs and NSLNs. We also evaluated the biological markers of the primary tumors by tissue microarray and immunohistochemistry. Of the 205 patients with SLN metastases, 89 patients (43.4%) had additional metastases in NSLNs. The following factors were found to be associated with NSLN metastases: peritumoral lymphovascular invasion (p=0.01), two or more metastatic SLNs (p 2.0 mm (p 2.0 cm showed more NSLN metastases, but the association was statistically insignificant (p=0.08). In contrast, NSLN metastases were not associated with histologic grade, histologic type, presence of extensive intraductal component, presence of high grade ductal carcinoma in situ and number of harvested SLNs. Biological markers such as E-cadherin, CD44, cyclin D1, p21, ER, PR, c-erbB2, p53, Ki-67, luminal (CK7, CK18, CK19) and basal (CK5, p63) markers were not useful predictors of NSLN metastasis in IBC patients with SLN metastases. Multivariate analysis revealed that SLN metastasis >2.0 mm (p=0.01), two or more metastatic SLNs (p=0.03) and extranodal extension (p<0.01) were independent predictors of NSLN metastasis. For the prediction of NSLN metastasis in IBC patients with SLN metastases, light microscopic evaluation of the number, size and extranodal extension of metastatic SLNs by hematoxylin and eosin staining appeared to be critical. However, the biological markers of primary tumor characterized by immunohistochemical staining, such as luminal and basal markers, hormone receptors, E-cadherin, CD44, cyclin D1, p21, c-erb-B2, p53 and Ki-67, did not appear to be helpful predictors.

/pdf/clinicopathological-parameters-and-biological-markers-4nokznlt9h.pdf

Clinicopathological parameters and biological markers predicting non-sentinel node metastasis in sentinel node-positive breast cancer patients

Introduction
Breast cancer with metastatic sentinel lymph nodes (SLN) may have clinico-pathologic factors associated with the presence of positive nonsentinel axillary nodes (NSLN). The aim of the present study was to determine factors that predict in-volvement of NSLN in breast cancer patients with positive SLN.

/pdf/predictors-of-positive-axillary-lymph-nodes-in-breast-cancer-40f2vdt2so.pdf

Predictors of positive axillary lymph nodes in breast cancer patients with metastatic sentinel lymph node

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Predictors of positive axillary lymph nodes in breast cancer patients with metastatic sentinel lymph node