Neoadjuvant and perioperative immunotherapy in resectable non-small cell lung cancer (NSCLC): A systematic review and extracted individual patient data meta-analysis.
Bibliographic record
Abstract
8045 Background: Neoadjuvant and perioperative immunotherapy are emerging approaches for resectable NSCLC. However, survival outcomes are still immature and subgroup analyses are underpowered. We report extracted individual patient (pt) data (eIPD) and trial-level (TL) meta-analyses of phase II and III randomized controlled trials (RCTs) in this setting. Methods: The systematic review included Cochrane, Embase, and major oncology conferences (ASCO annual meetings, ESMO meetings, WCLC meetings). Primary objectives were eIPD event-free survival (EFS) and overall survival (OS). Secondary objectives included eIPD and TL meta-analysis of subgroups. Kaplan-Meier plots of time-to-event outcomes were reconstructed with WebPlotDigitizer (v4.6, 2022), and eIPD was estimated with IPDfromKM (v0.1.10, 2020) stratified by study. Comparisons between arms were made in a 1-stage model with a Cox Proportional Hazards model stratified by study. The difference of restricted mean survival time (D-RMST), a quantification of the postponement of an event during a specified interval, was used to compare survival when the proportional hazard assumption (PHA) was violated, tau being the shortest follow-up time of the available trials. TL meta-analysis was performed with a random effects model. This meta-analysis was registered in the PROSPERO database under CRD42024502150. Results: Seven RCTs (AEGEAN, CM816, CM77T, KN671, NADIM II, NeoTorch, and Lei et al. [ESMO IOTECH 2022, #56O]) were identified, comprising 2995 pts. EFS analysis included all pts, with HR of 0.58 (0.52-0.65; p<0.01). OS analysis (1645 evaluable pts from 4 trials) showed a D-RMST of 5.17 mo. (p<0.01) due to PHA violation. Full eIPD analysis in the table. TL meta-analysis showed benefit in all subgroups, including PD-L1 expression, histology, stage, smoking status, sex, and age. TL EFS and OS results were comparable to results obtained from eIPD analysis, with low heterogeneity measures. Conclusions: This meta-analysis provides robust and nuanced insights into the positive impact of immunotherapy in resectable NSCLC. While evidence supports its efficacy, uncertainty surrounding the benefit in stage < III disease highlights the need for additional research and more mature results to guide clinical decision-making effectively. [Table: see text]
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".