Comparative Efficacy and Safety of Neoadjuvant Immunotherapy with Nivolumab vs. Pembrolizumab in Resectable Non-Small Cell Lung Cancer: A Systematic Review
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide. Immunotherapy has emerged as a promising treatment option due to its favorable toxicity profile. However, selecting the most appropriate immunotherapeutic agent for neoadjuvant use-aimed at curative intent in early-stage NSCLC-based on efficacy and safety remains a critical question. This review aims to compare the efficacy and safety profiles of nivolumab and pembrolizumab when used as neoadjuvant treatments in NSCLC. A systematic review was conducted across PubMed, Scopus, Wiley Online Library, ProQuest Dissertations and Theses Global, and Google Scholar, utilizing the search terms "Nivolumab OR Pembrolizumab AND Neoadjuvant Immunotherapy AND non-small cell lung cancer." Out of 1444 retrieved studies, 4 retrospective studies met the inclusion criteria by providing comparative data on nivolumab and pembrolizumab within the same study cohorts. Despite the critical risk of bias and the evidence quality ranging from moderate to very low across these studies, both nivolumab and pembrolizumab demonstrated efficacy rates exceeding 30% and maintained favorable safety profiles. There is no observed superiority between nivolumab and pembrolizumab in terms of efficacy and safety for the neoadjuvant treatment of early-stage 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".