Adjuvant Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors (TKIs) in Resected Non–Small Cell Lung Cancer (NSCLC)
Bibliographic record
Abstract
The role of adjuvant tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC) is not well defined. Recent randomized controlled trials showed a disease-free survival (DFS) benefit in patients harboring an epidermal growth factor receptor (EGFR) mutation. Yet, older trials on patients with any EGFR status did not demonstrate the same benefit. We aimed to assess the efficacy and safety of adjuvant TKIs in NSCLC patients. The electronic databases Medline (PubMed) and EMBASE were searched for relevant randomized controlled trials. Random effect models were used. The primary outcome was DFS measured as hazard ratio (HR). The secondary outcomes were overall survival (OS) measured as HR, 2-year DFS and toxicity expressed as risk ratio and odds ratio (OR), respectively. Subgroup analyses assessed DFS by trial design. Six trials incorporating 1860 patients were included. In patients harboring an EGFR mutation, adjuvant TKIs decreased the risk of disease recurrence by 48% (HR: 0.52, 95% confidence interval [CI]: 0.35-0.78), improved 2-year DFS (HR: 0.53, 95% CI: 0.43-0.66) but did not improve OS (HR: 0.64, 95% CI: 0.22-1.89). The risk of developing ≥grade 3 skin toxicity (OR: 6.07, 95% CI: 4.34-8.51) and diarrhea (OR: 4.05; 95% CI: 2.44-6.74) was increased. In subgroup analyses, the DFS benefit was more pronounced in trials using TKIs over chemotherapy compared with trials using TKIs postchemotherapy. In conclusion, adjuvant TKIs decrease the risk of recurrence in NSCLC patients harboring an EGFR mutation but do not improve OS. Longer follow-up is needed for a definitive assessment of OS and to define the role of adjuvant TKI for NSCLC in the clinical practice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".