Use of the Epidermal Growth Factor Receptor Inhibitors Gefitinib, Erlotinib, Afatinib, Dacomitinib, and Icotinib in the Treatment of Non-Small-Cell Lung Cancer: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: This systematic review addresses the use of epidermal growth factor receptor (egfr) inhibitors in three populations of advanced non-small-cell lung cancer (nsclc) patients-unselected, selected, and molecularly selected-in three treatment settings: first line, second line, and maintenance. METHODS: Ninety-six randomized controlled trials found using the medline and embase databases form the basis of this review. RESULTS: In the first-line setting, data about the efficacy of egfr tyrosine kinase inhibitors (tkis) compared with platinum-based chemotherapy are inconsistent. Results from studies that selected patients based on clinical characteristics are also mixed. There is high-quality evidence that an egfrtki is preferred over a platinum doublet as initial therapy for patients with an activating mutation of the EGFR gene. The egfrtkis are associated with a higher likelihood of response, longer progression-free survival, and improved quality of life. Multiple trials of second-line therapy have compared an egfrtki with chemotherapy. Meta-analysis of those data demonstrates similar progression-free and overall survival. There is consequently no preferred sequence for second-line egfrtki or second-line chemotherapy. The egfrtkis have also been evaluated as switch-maintenance therapy. No molecular marker could identify patients in whom a survival benefit was not observed; however, the magnitude of the benefit was modest. CONCLUSIONS: Determination of EGFR mutation status is essential to making appropriate treatment decisions in patients with nsclc. Patients who are EGFR mutation-positive should be treated with an egfrtki as first-line therapy. An egfrtki is still appropriate therapy in patients who are EGFR wild-type, but the selected agent should be administered as second- or third-line therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".