Efficacy and Safety of Perioperative Immunotherapy for Patients with Non-Small Cell Lung Cancer: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Background: The objective of this study is to indirectly compare the efficacy and safety of all currently available neoadjuvant chemoimmunotherapy and perioperative chemoimmunotherapy in randomized controlled trials (RCTs) involving patients with resectable non-small cell lung cancer (NSCLC) to identify optimal treatment regimens. Methods: Eligible studies evaluating neoadjuvant chemoimmunotherapy and perioperative chemoimmunotherapy-based regimens in resectable NSCLC patients were included. Clinical outcomes were extracted for event-free survival (EFS) and overall survival (OS), as well as the incidence of pathological complete response (pCR), major pathological response (MPR), any-grade adverse events (AEs), and treatment-related adverse events (TRAEs) in the Bayesian framework. A subgroup analysis of EFS was conducted according to PD-L1 expression, histological type and reaching pCR or not. Results: We selected eight RCTs involving 3113 patients. Our analysis found no significant differences between perioperative immunotherapy and neoadjuvant immunotherapy in terms of MPR (RR 0.72, 95% CI 0.39 –1.3), pCR (RR 0.73, 95% CI 0.24–2.3), EFS (HR 0.95, 95% CI 0.56–1.7), and OS (HR 95% CI 3.9–4.2). Subgroup analyses revealed that neoadjuvant immunotherapy demonstrated superiority in the programmed death-ligand 1 (PD-L1) high-expression cohort, the non-squamous cell carcinoma cohort, and the non-smoking cohort. Conversely, perioperative immunotherapy ranked first in the PD-L1 low-expression cohort, squamous cell carcinoma cohort, and non-pCR cohort. Conclusions: Our findings indicate that neoadjuvant immunotherapy and perioperative immunotherapy exhibit comparable efficacy in patients with NSCLC. These results provide valuable evidence for guiding the treatment of patients with resectable NSCLC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".