Molecular testing in stage I–III non-small cell lung cancer: Approaches and challenges
Bibliographic record
Abstract
Precision medicine in non-small cell lung cancer (NSCLC) is a rapidly evolving area, with the development of targeted therapies for advanced disease and concomitant molecular testing to inform clinical decision-making. In contrast, routine molecular testing in stage I-III disease has not been required, where standard of care comprises surgery with or without adjuvant or neoadjuvant chemotherapy, or concurrent chemoradiotherapy for unresectable stage III disease, without the integration of targeted therapy. However, the phase 3 ADAURA trial has recently shown that the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), osimertinib, reduces the risk of disease recurrence by 80% versus placebo in the adjuvant setting for patients with stage IB-IIIA EGFR mutation-positive NSCLC following complete tumor resection with or without adjuvant chemotherapy, according to physician and patient choice. Treatment with adjuvant osimertinib requires selection of patients based on the presence of an EGFR-TKI sensitizing mutation. Other targeted agents are currently being evaluated in the adjuvant and neoadjuvant settings. Approval of at least some of these other agents is highly likely in the coming years, bringing with it in parallel, a requirement for comprehensive molecular testing for stage I-III disease. In this review, we consider the implications of integrating molecular testing into practice when managing patients with stage I-III non-squamous NSCLC. We discuss best practices, approaches and challenges from pathology, surgical and oncology perspectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".