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

Authors :
Anneleen Reynders
Chantal Van Ongeval
Julie Soens
Karin Leunen
Alain Vanneste
Ignace Vergote
Marie-Rose Christiaens
Emi Yoshihara
Ann Smeets
G. Staelens
Philippe Moerman
Cecile Colpaert
Frédéric Amant
Annouschka Laenen
Olivier Brouckaert
Koen Van Eygen
Giuseppe Floris
Peter van Dam
Patrick Neven
Frederik Persyn
Other departments
Source :
Breast (Edinburgh, Scotland), 23(4), 453-459. Churchill Livingstone
Publication Year :
2013

Abstract

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. (C) 2014 Elsevier Ltd. All rights reserved

Details

ISSN :
15323080 and 09609776
Volume :
23
Issue :
4
Database :
OpenAIRE
Journal :
Breast (Edinburgh, Scotland)
Accession number :
edsair.doi.dedup.....c932accf81d1a5f082ff7e5fa4ebab4e