Advancements in Immunotherapy for Lung Cancer: A Meta-Analysis of Clinical Trial Outcomes.

Immune checkpoint inhibitors (ICIs) have changed therapeutic selections for advanced non-small cell lung cancer (NSCLC). However, comparative evidence versus chemotherapy through ICI monotherapy and chemo-immunotherapy combination strategies remains incomplete. For this purpose, we conducted a meta-analysis of randomized controlled trials (RCTs) to measure overall survival (OS) and progression-free survival (PFS) outcome improvements in ICI monotherapy or ICI-chemotherapy combined treatment. These interventions take place in adults (≥ 18 years) with advanced/metastatic NSCLC. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses directions were obeyed. PubMed, Scopus, and Web of Science were searched (2021-2024) for RCTs. A total of 7849 articles were retrieved. RCTs (n = 17) qualify inclusion. Hazard ratios (HRs) along with 95% confidence intervals (CIs) for OS and PFS were pooled using random-effects models. The heterogeneity was assessed by I2. Risk of bias was valued with RoB 2. Publication bias and leave-one-out sensitivity analyses were also estimated. ICI monotherapy RCTs (n = 5) significantly enhanced OS (pooled HR = 0.73, 95% CI: 0.64-0.85; p < 0.0001) and PFS (pooled HR = 0.69, 95% CI: 0.49-0.96) with increased heterogeneity (I2 = 90%). Combined ICI-chemotherapy RCTs (n = 12) produced great benefits, with a pooled OS (HR = 0.68, 95% CI: 0.61-0.75; p < 0.00001; I2 = 48%) and pooled PFS (HR = 0.55, 95% CI: 0.50-0.61; p < 0.00001; I2 = 56%). Subgroup analyses proposed durable effects in smaller trials and with pembrolizumab-based treatments. Risk-of-bias valuations underlined concern, primarily for deviations from therapeutic interventions. Sensitivity analyses set robustness, whereas publication bias was limited. ICI-based remedies, especially chemo-immunotherapy, significantly improve OS and PFS. Heterogeneity and experimental-level bias warrant cautious interpretation. Hence, large RCTs are important to choose the best treatment possibility.