Abstract
Background: Multiple first-line chemotherapy-based combination regimens are available for patients with extensive-stage small cell lung cancer (ES-SCLC), however, direct head-to-head comparisons remain limited. This network meta-analysis (NMA) aimed to indirectly compare the efficacy and safety of various first-line combination therapies. Methods: A comprehensive literature search was conducted across electronic databases and academic conference proceedings to identify eligible randomized controlled trials (RCTs). Bayesian network meta-analysis and systematic review were performed on the selected studies. Primary outcomes included overall survival (OS), progression-free survival (PFS), and objective response rate (ORR), along with adverse events of grade ⩾ 3 (grade ⩾ 3 AEs) and subgroup analyses. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42022360249). Results: The analysis included 12 randomized trials encompassing 5840 patients and 14 treatment regimens. The combination of benmelstobart, anlotinib and chemotherapy showed the most significant improvement in PFS (hazard ratio [HR] = 0.33, 95% confidence interval [CI] = 0.26-0.41) and OS (HR = 0.61, 95% CI = 0.47-0.79) compared with chemotherapy alone. This regimen ranked highest for PFS (Bayesian ranking probability 99%) and OS (39%). However, it was also associated with a higher risk of Grade ⩾ 3 AEs (HR = 2.01, 95% CI = 1.09-3.73). In patients with baseline liver metastases, this regimen provided the greatest PFS benefit (99%), whereas serplulimab plus chemotherapy offered the best OS (53%). Conversely, for patients with baseline brain metastases, combination therapy failed to demonstrate a significant survival benefit. Conclusions: For treatment-naïve ES-SCLC patients, benmelstobart plus anlotinib plus chemotherapy yielded the most favorable outcomes in terms of PFS, OS and ORR, but a less favorable safety profile. These findings support its use as a potent therapeutic option in few patient populations.