Lobectomy versus segmentectomy in patients with stage T (> 2 cm and ≤ 3 cm) N0M0 non-small cell lung cancer: a propensity score matching study

Journal of Cardiothoracic Surgery(2022)

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摘要
Background The safety and effectiveness of lung segmentectomy in patients with early non-small cell lung cancer (NSCLC) remains controversial. We have therefore reviewed the clinicopathologic characteristics and survival outcomes of patients treated with lobectomy or segmentectomy for early T (> 2 and ≤ 3 cm) N0M0 NSCLC. Methods We obtained data from the Surveillance, Epidemiology, and End Results database for patients who underwent lobectomy or segmentectomy between 2004 and 2015. To reduce bias and imbalances between the treatment groups, propensity score matching analysis was performed. We used Kaplan–Meier curves to estimate overall survival (OS) and lung cancer-specific survival (LCSS). We conducted univariate and multivariate Cox proportional hazards regression analyses to identify independent prognostic factors for OS and cancer-specific survival, and applied the Cox proportional hazards model to create forest plots. Results Before matching, both univariate and multivariate Cox regression analyses revealed that patients who underwent lobectomy exhibited better OS ( P < 0.001) and LCSS ( P = 0.001) than patients who underwent segmentectomy. However, after matching, survival differences between the groups were not significant; OS ( P = 0.434) and LCSS ( P = 0.593). Regression analyses revealed that age and tumor grade were independent predictors of OS and LCSS ( P < 0.05). Conclusions Patients with stage T (> 2 and ≤ 3 cm) N0M0 NSCLC undergoing segmentectomy can obtain OS and LCSS similar to those obtained with lobectomy. Further studies are required considering the solid component effects and pathologic tumor types regarding segmentectomies. Additional long-term survival and outcome analyses should be conducted with larger cohorts.
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关键词
Forest plots, Non-small cell lung cancer (NSCLC), Propensity score matching (PSM), Surveillance, Epidemiology, and End Results (SEER) database, Survival analysis
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