Title
Prediction of non-sentinel lymph node involvement in breast cancer patients with a positive sentinel lymph nodePrediction of non-sentinel lymph node involvement in breast cancer patients with a positive sentinel lymph node
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Research group
Molecular Imaging, Pathology, Radiotherapy & Oncology (MIPRO)
Faculteit Geneeskunde
Publication type
article
Publication
Edinburgh,
Subject
Human medicine
Source (journal)
Breast. - Edinburgh
Volume/pages
23(2014):3, p. 453-459
ISSN
0960-9776
ISI
000338820200023
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
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.
E-info
https://repository.uantwerpen.be/docman/iruaauth/acbc44/73c10567129.pdf
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