Update on the Management of Stage III NSCLC: Navigating a Complex and Heterogeneous Stage of Disease
Bibliographic record
Abstract
Background: Stage III nonsmall cell lung cancer (NSCLC) represents a heterogeneous group of patients. Many patients are treated with curative intent multimodality therapy, either surgical resection plus systemic therapy or chemoradiation plus immunotherapy. However, many patients are not suitable for curative intent therapy and are treated with palliative systemic therapy or best supportive care. Methods: This paper is a review of recent advances in the management of patients with curative intent disease. Results: There have been significant advances in curative intent therapy for patients with stage III NSCLC in recent years. These include both adjuvant and neoadjuvant systemic therapies. For patients with resectable NSCLC, two trials have demonstrated that adjuvant atezolizumab or pembrolizumab, following chemotherapy, significantly improved disease-free survival (DFS). In patients with tumours harbouring a common mutation of the EGFR gene, adjuvant osimertinib therapy was associated with a large improvement in both DFS and overall survival (OS). Five randomized trials have evaluated chemotherapy plus nivolumab, pembrolizumab, durvalumab, or toripalimab, either as neoadjuvant or perioperative (neoadjuvant plus adjuvant) therapy. All five trials show significant improvements in the rate of pathologic complete response (pCR) and event-free survival (EFS). OS data are currently immature. This would now be considered the standard of care for resectable stage III NSCLC. The addition of durvalumab to chemoradiation has also become the standard of care in unresectable stage III NSCLC. One year of consolidation durvalumab following concurrent chemoradiation has demonstrated significant improvements in both progression-free and overall survival. Conclusions: Immune checkpoint inhibitor (ICI) therapy has become a standard recommendation in curative intent therapy for stage III 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".