Epidermal Growth Factor Receptor Inhibitors in the Treatment of Non-small Cell Lung Cancer
Bibliographic record
Abstract
Inhibition of the epidermal growth factor receptor (EGFR) has become a standard target in the treatment of non-small cell lung cancer (NSCLC). This chapter summarizes the clinical trials that have been performed with small molecule tyrosine kinase inhibitors (TKI) and monoclonal antibodies targeting EGFR in NSCLC. Erlotinib has established second- and third-line efficacy following the phase III BR.21 study, results not seen in the corresponding ISEL trial with gefitinib. However, in the INTEREST trial gefitinib demonstrated non-inferiority to docetaxel in the second-line setting, and as first-line therapy may be better than chemotherapy in selected patients. Cetuximab also modestly prolongs survival when combined with chemotherapy as first-line treatment in patients with EGFR expressing tumors. Numerous other TKIs and monoclonal antibodies have demonstrated clinical activity in early phase trials. Novel TKIs may have the ability to overcome resistance to first generation TKI therapy. Furthermore, there are encouraging studies combining EGFR inhibitors and anti-angiogenesis drugs such as bevacizumab. In conclusion, EGFR inhibition, by a range of strategies, remains a central node in the treatment of NSCLC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.010 | 0.010 |
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".