Phase II Study of Preoperative Gefitinib in Clinical Stage I Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have proven efficacy in advanced non-small-cell lung cancer (NSCLC). Their role in early-stage NSCLC has not been established. Our purpose was to explore the use of preoperative gefitinib in clinical stage I NSCLC to assess tumor response, toxicity, and clinical and molecular predictors of response. PATIENTS AND METHODS: Patients received gefitinib 250 mg/d for up to 28 days, followed by mediastinoscopy and surgical resection in an open-label, single-arm study. Tumor response was evaluated by Response Evaluation Criteria in Solid Tumors. Blood samples and tumor biopsies were collected and analyzed for transforming growth factor alpha level, EGFR protein expression, EGFR gene copy number, and EGFR (exon 19 to 21) and KRAS mutations. RESULTS: Thirty-six patients completed preoperative treatment (median duration, 28 days; range, 27 to 30 days). Median follow-up time is 2.1 years (range, 0.86 to 3.46 years). Three patients experienced grade 3 toxicities (rash, diarrhea, and elevated ALT). Tumors demonstrated EGFR-positive protein expression in 83%, high gene copy number in 59%, EGFR mutations in 17%, and KRAS mutations in 17%. Tumor shrinkage was more frequent among women and nonsmokers. Partial response was seen in four patients (11%), and disease progression was seen in three patients (9%). The strongest predictor of response was EGFR mutation. CONCLUSION: Preoperative window therapy with gefitinib is a safe and feasible regimen in early NSCLC and provides a trial design that may better inform predictors of treatment response or sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".