鳞状非小细胞肺癌的最佳药物治疗方案:随机试验的系统综述和贝叶斯网络荟萃分析
Optimal drug regimens for squamous non-small cell lung cancer: a systematic review and a Bayesian network meta-analysis of randomized trials.
Optimal drug regimens for squamous non-small cell lung cancer: a systematic review and a Bayesian network meta-analysis of randomized trials.
目前,鳞状非小细胞肺癌(sqNSCLC)患者的治疗选择包括免疫检查点抑制剂联合化疗、免疫治疗单药以及含铂双药化疗等。然而,这些方案在相对疗效和安全性方面仍缺乏系统比较。本研究旨在通过网络荟萃分析全面评估不同治疗策略的临床获益[如总生存期(OS)、无进展生存期(PFS)]及不良事件发生率,为sqNSCLC患者的个体化治疗决策提供循证依据。我们检索了PubMed、Embase、Cochrane Library和Web of Science自建库至2025年5月3日的合格随机对照试验(RCT)。主要结局指标为PFS和OS;次要结局包括客观缓解率(ORR)和≥3级治疗相关不良事件(≥3 TRAEs)。采用Stata 16.0和R 4.4.0进行网络荟萃分析。共纳入32项RCT,涉及9,652例受试者。本研究分析了18种不同的治疗策略。网络荟萃分析显示,对于PFS,紫杉醇类双药化疗联合程序性细胞死亡-1及程序性死亡配体1[PD-(L)1]检查点抑制剂[累积排序曲线下面积(SUCRA):96.52%]和吉西他滨类双药化疗联合PD-(L)1检查点抑制剂(SUCRA:85.16%)最为有效。对于OS,紫杉醇类双药化疗联合Toll样受体-2激动剂(CADI-05)(SUCRA:92.11%)、紫杉醇类双药化疗联合PD-(L)1及细胞毒性T淋巴细胞相关蛋白4(CTLA-4)检查点抑制剂(SUCRA:89.65%)以及紫杉醇类双药化疗联合PD-(L)1检查点抑制剂(SUCRA:83.58%)位列前三。紫杉醇类双药化疗联合PD-(L)1检查点抑制剂(SUCRA:87.00%)与ORR改善最佳相关。对于sqNSCLC的一线治疗,当前证据支持将紫杉醇类双药化疗联合PD-(L)1检查点抑制剂作为首选治疗方案之一。然而,仍需更多高质量试验来证实这些发现。
Current treatment options for patients with squamous non-small cell lung cancer (sqNSCLC) include immune checkpoint inhibitors combined with chemotherapy, monotherapy with immune therapy, and platinum-based doublet chemotherapy, among others. However, there is still a lack of systematic comparisons regarding the relative efficacy and safety of these regimens. This study aims to comprehensively assess the clinical benefits [such as overall survival (OS), progression-free survival (PFS)] and adverse event rates across different treatment strategies via a network meta-analysis, providing evidence-based guidance for individualized treatment decisions in sqNSCLC patients. We searched PubMed, Embase, Cochrane Library, and Web of Science from inception to May 3, 2025 for eligible randomized controlled trials (RCTs). The primary outcome measures were PFS and OS; secondary outcomes included objective response rate (ORR) and grade ≥3 treatment-related adverse events (≥3 TRAEs). A network meta-analysis was carried out using Stata 16.0 and R 4.4.0. A total of 32 RCTs, involving 9,652 participants, were included. This study analyzed 18 different treatment strategies. The network meta-analysis showed that for PFS, paclitaxel-based doublet chemotherapy combined with programmed cell death-1 and programmed death-ligand 1 [PD-(L)1] checkpoint inhibitors [surface under the cumulative ranking curve (SUCRA): 96.52%] and gemcitabine-based doublet chemotherapy combined with PD-(L)1 checkpoint inhibitors (SUCRA: 85.16%) were the most effective. For OS, paclitaxel-based doublet chemotherapy combined with a Toll-like receptor-2 agonist (CADI-05) (SUCRA: 92.11%), paclitaxel-based doublet chemotherapy combined with PD-(L)1 and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) checkpoint inhibitors (SUCRA: 89.65%), and paclitaxel-based doublet chemotherapy combined with PD-(L)1 checkpoint inhibitors (SUCRA: 83.58%) were the top three choices. The combination of paclitaxel-based doublet chemotherapy with PD-(L)1 checkpoint inhibitors (SUCRA: 87.00%) was associated with the best improvement in ORR. For first-line treatment of sqNSCLC, the current evidence supports the use of paclitaxel-based doublet chemotherapy combined with PD-(L)1 checkpoint inhibitors as one of the preferred treatment options. However, more high-quality trials are needed to confirm these findings.
Yan Ye, Yaqian Li, Nuoyu Yan, Lan Sun. Optimal drug regimens for squamous non-small cell lung cancer: a systematic review and a Bayesian network meta-analysis of randomized trials.. Journal of thoracic disease. 2026; doi:10.21037/jtd-2026-1-0152.
Yan Ye, Yaqian Li, Nuoyu Yan, Lan Sun (2026). Optimal drug regimens for squamous non-small cell lung cancer: a systematic review and a Bayesian network meta-analysis of randomized trials.. Journal of thoracic disease. https://doi.org/10.21037/jtd-2026-1-0152
Yan Ye, Yaqian Li, Nuoyu Yan, Lan Sun. Optimal drug regimens for squamous non-small cell lung cancer: a systematic review and a Bayesian network meta-analysis of randomized trials.[J]. Journal of thoracic disease, 2026 doi:10.21037/jtd-2026-1-0152.
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