The lesser the better? A systematic review and meta-analysis of resection strategy in lung neuroendocrine tumors

越少越好?肺神经内分泌肿瘤切除策略的系统评价和荟萃分析

阅读:1

Abstract

INTRODUCTION: Lung neuroendocrine tumors (LNETs) are rare, with surgical resection as the mainstay of treatment, although the optimal extent remains uncertain. Herein, we present the first meta-analysis to assess the effect of resection extent (lobar vs. sub-lobar) on overall survival. METHODS: We conducted a systematic review of the literature to identify studies comparing overall survival following lobectomy versus sub-lobar resection in LNETs. An inverse-variance meta-analysis was performed, and a Cox regression model was applied to reconstructed time-to-event data estimated from published Kaplan-Meier curves to generate pooled survival estimates. RESULTS: Six studies encompassing 3,700 patients (lobectomy, n = 2,409; sub-lobar resection, n = 1,291) were included in the final analysis. The pooled 5-year overall survival for the entire cohort was 78.8% (95% CI, 76.6-81.1). No statistically significant difference in overall survival was observed between lobectomy and sub-lobar resection (HR = 1.21; 95% CI, 0.80-1.83; I(2) = 0%). Segmentectomy and lobectomy demonstrated comparable survival (p = 0.38), whereas wedge resection was associated with higher mortality (HR = 2.02; 95% CI, 1.64-2.49; I(2) = 0%). Sampling of >10 lymph nodes was more frequent in lobectomy than sub-lobar resection (29.1% [95% CI, 0.8-95.3] vs 7.4% [95% CI, 0.01-98], respectively), likely contributing to the higher rate of nodal pathologic upstaging observed in the lobectomy group (6.2% [95% CI, 0.2-64.9] vs 2.2% [95% CI, 0-99]). CONCLUSION: In this first meta-analysis of surgical resection for LNETs, sub-lobar resection and lobectomy showed no clear difference in overall survival. Adequate lymph node assessment remains essential, irrespective of the surgical approach.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。