Neoadjuvant Nivolumab Plus Ipilimumab Versus Chemotherapy in Resectable Lung Cancer
Bibliographic record
Abstract
PURPOSE: Neoadjuvant immune checkpoint blockade with nivolumab plus ipilimumab improves overall survival (OS) in non-small cell lung cancer (NSCLC); however, randomized data for resectable lung cancer are limited. We report results from the exploratory concurrently randomized nivolumab plus ipilimumab and chemotherapy arms of the international phase III CheckMate 816 trial. METHODS: Adults with stage IB-IIIA (American Joint Committee on Cancer seventh edition) resectable NSCLC received three cycles of nivolumab once every 2 weeks plus one cycle of ipilimumab or three cycles of chemotherapy (on day 1 or days 1 and 8 of each 3-week cycle) followed by surgery. Analyses included event-free survival (EFS), OS, pathologic response, surgical outcomes, biomarker analyses, and safety. RESULTS: A total of 221 patients were concurrently randomly assigned to nivolumab plus ipilimumab (n = 113) or chemotherapy (n = 108). At a median follow-up of 49.2 months, the median EFS was 54.8 months (95% CI, 24.4 to not reached [NR]) with nivolumab plus ipilimumab versus 20.9 months (95% CI, 14.2 to NR) with chemotherapy (HR, 0.77 [95% CI, 0.51 to 1.15]); 3-year EFS rates were 56% versus 44%. Higher rates of EFS events were initially seen, with later benefit favoring nivolumab plus ipilimumab; 3-year OS rates were 73% versus 61% (HR, 0.73 [95% CI, 0.47 to 1.14]); pathologic complete response rates were 20.4% versus 4.6%, respectively. In the respective arms, 83 (74%) and 82 patients (76%) underwent definitive surgery. Grade 3-4 treatment-related adverse events occurred in 14% and 36% of patients, respectively. CONCLUSION: Neoadjuvant nivolumab plus ipilimumab showed potential long-term clinical benefit versus chemotherapy, despite early crossing of EFS curves in the preoperative phase and a lower rate of high-grade toxicity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".