Treatment Sequencing in Resectable Lung Cancer: The Good and the Bad of Adjuvant Versus Neoadjuvant Therapy
Bibliographic record
Abstract
The treatment scenario for patients with resectable non-small cell lung cancer has changed dramatically with the incorporation of immunotherapy. The introduction of immunotherapy into treatment algorithms has yielded improved clinical outcomes in several phase II and III trials in both adjuvant (Impower010 and PEARLS) and neoadjuvant settings (JHU/MSK, LCMC3, NEOSTAR, Columbia/MGH, NADIM, and CheckMate-816), leading to new U.S. Food and Drug Administration approvals in this sense. Different treatment options are now available for patients, making the optimal treatment scenario a matter of intense debate. In this review, we summarize the main results concerning treatment sequencing in resectable non-small cell lung cancer from the past 30 years in the preimmunotherapy era, focusing on recent advances after incorporation of immunotherapy. Finally, the utility of several parameters (PD-L1, tumor mutational burden, radiomics, circulating tumor DNA, T-cell receptor, and immune populations) as predictive biomarkers for therapy personalization is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".