Adjuvant therapies in stages I–III epidermal growth factor receptor-mutated lung cancer: current and future perspectives
Bibliographic record
Abstract
Surgical resection followed by adjuvant cisplatin-based chemotherapy is the recommended treatment for patients with completely resected stage IB-IIIA non-small cell lung cancer (NSCLC). Even with the best management, recurrence is common and increases with disease stage (stage I: 26-45%; stage II: 42-62%; stage III: 70-77%). For patients with metastatic lung cancer and tumours that harbour epidermal growth factor receptor (EGFR) mutations, EGFR-tyrosine kinase inhibitors (TKIs) have improved survival. Their effectiveness in advanced stages of NSCLC raises the possibility that these agents may improve outcomes for patients with resectable EGFR-mutated lung cancer. In the ADAURA study, adjuvant osimertinib provided a significant improvement in disease-free survival (DFS) and reduced central nervous system (CNS) disease recurrence in patients with resected stage IB-IIIA EGFR-mutated NSCLC, with or without prior adjuvant chemotherapy. To reap the maximum benefits of EGFR-TKIs for patients with lung cancer, the early and rapid identification of EGFR mutations [and other oncogenic drivers, such as programmed cell death-ligand 1 (PD-L1), with matched targeted therapies] in diagnostic pathologic specimens has become essential. To ensure patients receive the most appropriate treatment, routine, comprehensive histological, immunohistochemical, and molecular analyses (with multiplex next generation sequencing) should be undertaken at the time of diagnosis. The potential for personalised treatments to cure more patients with early-stage lung cancer can only be realised if all therapies are considered when the care plan is formulated, by the multi-specialty experts managing patients. In this review, we discuss the progress and prospects for adjuvant treatments as part of a comprehensive plan of care for patients with resected stages I-III EGFR-mutated lung cancer, and explore how the field could go beyond DFS and overall survival to make cure a more frequent outcome of treatment in patients with resected EGFR-mutated lung cancer.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".