Clinicopathologic features of metastasis in nonsentinel lymph nodes of breast carcinoma patients
Open Access
- 17 November 2003
- Vol. 98 (11) , 2307-2315
- https://doi.org/10.1002/cncr.11803
Abstract
BACKGROUND In breast carcinoma patients with a positive sentinel lymph node (SN), the value of complete axillary lymph node dissection has been questioned. Multiple published reports have attempted to identify clinicopathologic characteristics of the primary tumor and SN that are associated with an increased likelihood of positive nonsentinel lymph nodes (NSN). Because of differences in lymph node evaluation techniques and limited patient numbers in each study, the authors performed a meta‐analysis to assess the regularity and relative strength of association between various characteristics and the risk of NSN metastasis. METHODS A MEDLINE search identified 15 candidate studies, 11 of which met the criteria for analysis. General elements of the studies, the pathologic characteristics evaluated, and the results for selected characteristics were compared. Original data were abstracted from each study and used to calculate odds ratios. The Mantel–Haenszel common odds ratios were calculated to determine the relative strength of the associations. RESULTS Despite methodologic differences, the correlation between positive NSNs and certain pathologic characteristics was found to be remarkably similar among studies. The 5 individual characteristics found to be associated with the highest likelihood of NSN metastasis are SN metastasis > 2 mm in size, extranodal extension in the SN, tumor size > 2 cm, > 1 positive SN, and lymphovascular invasion in the primary tumor. CONCLUSIONS There is general concordance among studies regarding the association between pathologic characteristics and NSN metastasis in breast carcinoma patients with a positive SN. The pooled analysis identified those factors with the strongest associations that should be evaluated routinely in SN specimens and included in prospective databases for the development of a predictive model. Cancer 2003. © 2003 American Cancer Society.Keywords
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