Abstract
Background: Whether lung segmentectomy is a safe and effective surgical treatment in patients with early non-small cell lung cancer (NSCLC) remains controversial. We have therefore reviewed the clinicopathologic characteristics and survival outcomes of patients receiving a lobectomy vs. segmentectomy to treat early T (>2 cm and ≤3 cm) N0M0 NSCLC.Methods: We obtained data from the Surveillance, Epidemiology, and End Results (SEER) database for patients who underwent lobectomy or segmentectomy between 2004 and 2015. To reduce bias and imbalance between the treatment groups, propensity score matching (PSM) analysis was performed. We used Kaplan-Meier curves to estimate overall survival (OS) and lung cancer-specific survival (LCSS), performed univariate and multivariate Cox proportional hazards regression analyses to identify independent prognostic factors for OS and CSS, and applied the Cox proportional hazards model to create forest plots. Results: A total of 5783 patients from the SEER database were included. Of these, 5531 patients underwent lobectomy, and 252 patients underwent segmentectomy. Before matching, both univariate and multivariate Cox regression analyses showed that patients who underwent lobectomy had better OS (hazard ratio [HR]: 1.561; 95% confidence interval [CI] 1.292-1.885; P <0.001) and LCSS (HR: 1.551; 95% CI 1.198-2.009; P=0.001) than patients who underwent segmentectomy. However, survival differences between the groups were not significant; OS (P=0.160) and LCSS (P=0.097) after matching. Regression analyses revealed that age, sex, lymph node dissection, and grade were independent predictors of OS and LCSS (P <0.05).Conclusions: For patients with stage T (>2 cm and ≤3 cm) N0M0 non-small cell lung cancer, segmentectomy can achieve the same OS and LCSS compared with lobectomy. A large number of patients require further long-term follow-up analyses.
A nomogram model to predict death rate among non-small cell lung cancer (NSCLC) patients with surgery in surveillance, epidemiology, and end results (SEER) database
