Emerging Evidence from Landmark Clinical Trials on Perioperative Immunotherapy in Resectable Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) remains one of the most prevalent cancers worldwide, with high rates of local and distant recurrence limiting survival even after curative-intent surgical resection. Traditional adjuvant chemotherapy benefits only 5% of patients, and as such additional treatment modalities are urgently needed to improve NSCLC patient outcomes. Systemic therapy with PD1 and PDL1 immune check-point inhibitors (ICIs) has emerged as a promising treatment option in many types of solid malignancies, including lung cancer. Encouraging results from immunotherapy trials in metastatic lung cancer populations, and now newer results from ongoing clinical trials in early stage locally advanced lung cancers, suggested an evolving role for perioperative immune checkpoint inhibition in resectable NSCLC. In this review we examine the latest advances in the landscape of clinical trials on immunotherapy in resectable NSCLC. We discuss the key findings and specific clinical challenges related to neoadjuvant administration of these immune therapies in the CheckMate816, Impower030, AEGEAN and KEYNOTE671 phase III clinical trials. The role of adjuvant ICI is also discussed examining the ANVIL, Impower010, and PEARLS trials. By understanding the remaining unanswered questions and clinical dilemmas that exist in this rapidly evolving field for ICIs in early stage NSCLC, clinicians may provide patients options which may markedly improve survival outcomes for this life-threatening disease.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".