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Cancer. 2010 Jun 1;116(11):2560-70. doi: 10.1002/cncr.25032.

Clinical and pathologic factors that predict lymph node yield from surgical specimens in colorectal cancer: a population-based study.

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Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.



The National Quality Forum endorses the recommendation of examining at least 12 lymph nodes (LNs) from colorectal cancer (CRC) specimens. However, heterogeneity in LN harvest exists. The objective of this study was to investigate the clinicopathologic factors that influence LN yield.


The authors used the Surveillance, Epidemiology, and End Results database to identify patients who were diagnosed with stage I, II, and III CRC between 1994 and 2005. Poisson regression was used to model the number of LNs examined as a function of individual clinicopathologic factors, including age, sex, race, year of diagnosis, geographic region, anatomic site, preoperative radiation, tumor size, tumor classification, tumor differentiation, and LN positivity.


In total, 153,483 patients with CRC were identified. The mean number of LNs examined (+/- standard deviation) was 12 (+/-9.3). Separate multivariate analyses revealed that age, year of diagnosis, tumor size, and tumor classification were significant predictors of LN yield for colon and extraperitoneal rectal cancers (P < .01 for all covariates). Tumor location and radiotherapy were significant predictors of LN yield in patients with colon cancer and rectal cancer, respectively. Overall LN yields increased between 2% and 3% annually.


Despite the increasing yields observed over time, patients with rectal cancer and older patients who had distally located, early colon cancer were less likely to meet the benchmark yield of 12 LNs. Further investigation into how LN yield is influenced by alterable factors, such as the extent of mesenteric resection and the pathologic technique, as well as nonalterable factors, such as patient age and tumor location, may reveal innovative ways to improve current staging methods.

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