Impact of Examined Lymph Node Count on Precise Staging and Long-Term Survival of Resected Non-Small-Cell Lung Cancer: A Population Study of the US SEER Database and a Chinese Multi-Institutional Registry

Abstract

Purpose We investigated the correlation between the number of examined lymph nodes (ELNs) and correct staging and long-term survival in non-small-cell lung cancer (NSCLC) by using large databases and determined the minimal threshold for the ELN count. Methods Data from a Chinese multi-institutional registry and the US SEER database on stage I to IIIA resected NSCLC (2001 to 2008) were analyzed for the relationship between the ELN count and stage migration and overall survival (OS) by using multivariable models. The series of the mean positive LNs, odds ratios (ORs), and hazard ratios (HRs) were fitted with a LOWESS smoother, and the structural break points were determined by Chow test. The selected cut point was validated with the SEER 2009 cohort. Results Although the distribution of ELN count differed between the Chinese registry (n = 5,706) and the SEER database (n = 38,806; median, 15 versus seven, respectively), both cohorts exhibited significantly proportional increases from N0 to N1 and N2 disease (SEER OR, 1.038; China OR, 1.012; both P < .001) and serial improvements in OS (N0 disease: SEER HR, 0.986; China HR, 0.981; both P < .001; N1 and N2 disease: SEER HR, 0.989; China HR, 0.984; both P < .001) as the ELN count increased after controlling for confounders. Cut point analysis showed a threshold ELN count of 16 in patients with declared node-negative disease, which were examined in the derivation cohorts (SEER 2001 to 2008 HR, 0.830; China HR, 0.738) and validated in the SEER 2009 cohort (HR, 0.837). Conclusion A greater number of ELNs is associated with more-accurate node staging and better long-term survival of resected NSCLC. We recommend 16 ELNs as the cut point for evaluating the quality of LN examination or prognostic stratification postoperatively for patients with declared node-negative disease.

Fig 1.

Distribution of the number of harvested lymph nodes in the China registry and the SEER database. ELN, examined lymph node.

Fig 2.

LOWESS smoother fitting curves of stage migration and overall survival and determination of structural break points with use of the Chow test. The fitting bandwidth was 2/3. (A) and (B) Stage migration was estimated by logistic regression after adjusting for T staging, N staging, histology, tumor location, and operation type in both cohorts. (C) and (D) Overall survival was estimated by using the Cox proportional hazards regression model after adjusting for sex, age, T staging, histology, and operation type. ELN, examined lymph node.

Fig 2.

LOWESS smoother fitting curves of stage migration and overall survival and determination of structural break points with use of the Chow test. The fitting bandwidth was 2/3. (A) and (B) Stage migration was estimated by logistic regression after adjusting for T staging, N staging, histology, tumor location, and operation type in both cohorts. (C) and (D) Overall survival was estimated by using the Cox proportional hazards regression model after adjusting for sex, age, T staging, histology, and operation type. ELN, examined lymph node.

Fig 3.

Stratification of overall survival among patients with node-negative non–small-cell lung cancer at the cut point of the number of harvested lymph nodes (16) on the basis of multivariable adjustments (other covariates were sex, age, T staging, histology, and operation type). ELN, examined lymph node.

Fig A1.

(A) LOWESS smoother fitting curves of estimated probabilities of undetected positive lymph nodes (LNs) in patients with declared node-negative disease. (B) Mean number of positive LNs as well as determination of structural break points by Chow test (fitting bandwidth = 2/3). ELN, examined lymph node.

Fig A2.

Stratification of disease-free survival among patients with node-negative non–small-cell lung cancer at the cut point of the number of harvested lymph nodes (16) on the basis of multivariable adjustments (other covariates: sex, age, T staging, histology, and operation type) of the single-center cohort (The First Affiliated Hospital of Guangzhou Medical University).