Perioperative Chemo/Immunotherapies in Lung Cancer: A Critical Review on the Value of Perioperative Sequences
Bibliographic record
Abstract
Resectable non-small cell lung cancer (NSCLC) continues to pose significant challenges with high recurrence and mortality rates, despite traditional platinum-based chemotherapy yielding only an approximate 5% improvement in 5-year overall survival when administered preoperatively or postoperatively. In recent years, the integration of immune checkpoint inhibitors (ICIs), such as nivolumab, durvalumab and pembrolizumab, with platinum-based regimens in the perioperative setting has emerged as a transformative strategy. Our comprehensive review, based on a systematic bibliographic search of PubMed, Google Scholar, EMBASE, Cochrane Library, and clinicaltrials.gov, targeting pivotal clinical trials from the past two decades, examines the impact of these neoadjuvant and adjuvant chemoimmunotherapy approaches on major pathological response rates and overall survival in early-stage NSCLC. Although these perioperative strategies represent a paradigm shift in treatment, promising durable responses are offset by persistent recurrence, emphasizing the necessity for optimized treatment sequencing, duration, and the identification of predictive biomarkers. Collectively, our findings underscore the critical role of the perioperative schema, particularly the neoadjuvant component, which enables the evaluation of novel biomarkers as surrogates for overall survival, in improving patient outcomes and delineating future research directions aimed at reducing mortality and enhancing the quality of life for 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".