Treatments for patients with advanced neuroendocrine tumors: a network meta-analysis

晚期神经内分泌肿瘤患者的治疗:一项网络荟萃分析

阅读:1

Abstract

BACKGROUND: It remains unknown which is the most effective regimen among the available therapies for advanced well-differentiated neuroendocrine tumors (NETs). We performed a network meta-analysis to address this important issue. METHODS: PubMed, Embase, Web of Science, Cochrane Library, and major international scientific meetings were searched for relevant randomized controlled trials (RCTs). Progression-free survival (PFS) data was the primary outcome of interest, and overall survival (OS) and serious adverse events (SAEs) were the secondary outcomes of interests, reported as hazard ratio (HR), or odds ratio (OR) and 95% confidence intervals (CIs). RESULTS: Included in the meta-analysis were 21 eligible articles reporting 15 RCTs with a total of 2922 patients randomized to receive 11 treatments. Peptide receptor radionuclide therapy (PRRT) showed significant PFS advantage over somatostatin analogs (SSA) (HR = 0.21, 95% CI: 0.11-0.41), everolimus (HR = 0.25, 95% CI: 0.11-0.53), sunitinib (HR = 0.29, 95% CI: 0.10-0.82), everolimus+SSA (HR = 0.26, 95% CI: 0.12-0.54), and everolimus+bevacizumab (HR = 0.31, 95% CI: 0.11-0.82). OS findings were not significantly different between treatments. In terms of treatment rankings of PFS, PRRT had the highest probability (96%) of being the most effective treatment, followed by SSA+bevacizumab (86%) and SSA+interferon-α (IFN-α) (78%). As for toxicity, risk of SAEs was similar between the three treatments. Based on the benefit-risk ratio, PRRT, SSA+bevacizumab, and SSA+IFN-α seemed to be the best, second-, and third-best treatment, respectively. CONCLUSIONS: PRRT is likely to be the most preferable treatment for patients with advanced well-differentiated NETs. SSA+bevacizumab and SSA+IFN-α also seem to be more effective regimens with limited risk of SAEs.

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。