An Updated Review of Management of Resectable Stage III NSCLC in the Era of Neoadjuvant Immunotherapy
Bibliographic record
Abstract
Immune-checkpoint inhibitors (ICIs) have an established role in the treatment of locally advanced and metastatic non-small cell lung cancer (NSCLC). ICIs have now entered the paradigm of early-stage NSCLC. The recent evidence shows that the addition of ICI to neoadjuvant chemotherapy improves the pathological complete response (pCR) rate and survival rate in early-stage resectable NSCLC and is now a standard of care option in this setting. In this regard, stage III NSCLC merits special consideration, as it is heterogenous and requires a multidisciplinary approach to management. As the neoadjuvant approach is being adopted widely, new challenges have emerged and the boundaries for resectability are being re-examined. Consequently, it is ever more important to carefully individualize the treatment strategy for each patient with resectable stage III NSCLC. In this review, we discuss the recent literature in this field with particular focus on evolving definitions of resectability, T4 disease, N2 disease (single and multi-station), and nodal downstaging. We also highlight the controversy around adjuvant treatment in this setting and discuss the selection of patients for adjuvant treatment, options of salvage, and next line treatment in cases of progression on/after neoadjuvant treatment or after R2 resection. We will conclude with a brief discussion of predictive biomarkers, predictive models, ongoing studies, and directions for future research in this space.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".