Kinase Inhibitors or Docetaxel in Second-Line Treatment of EGFR Wild-Type Non-small-Cell Lung Cancer: A Retrospective Real-World Practice Review at a Single Tertiary Care Centre
Bibliographic record
Abstract
BACKGROUND: Treatment for advanced non-small-cell lung cancer (nsclc), especially in patients with wild-type EGFR, remains limited. Recently, erlotinib, a tyrosine kinase inhibitor (tki) targeting EGFR mutation, was approved as second-line treatment in EGFR wild-type nsclc. Despite evidence of better overall survival (os) with chemotherapy than with tki in second-line treatment, data on the use of tki in the real-life clinical setting remain limited. The present practice review of tki use for second- and third-line treatment in EGFR wild-type nsclc also compares clinical outcomes for tki and single-agent docetaxel as second-line treatment. METHODS: Our retrospective cohort study included patients with EGFR wild-type nsclc treated at the Jewish General Hospital (Montreal, QC) between 2003 and 2013. Patients received a tki (erlotinib or gefitinib) in the second and third line or docetaxel in the second line. For each group, we determined os, disease control rate, progression-free survival (pfs), and event-free survival (efs). RESULTS: The tki group included 145 patients, with 92 receiving second-line treatment. In the control group, 53 patients received docetaxel as second-line therapy. In the tki group, os was 6.0 months; pfs, 2.7 months; and efs, 3.0 months. Comparing second-line treatments, os was 5.3 and 5.0 months respectively (p = 0.88), pfs was 2.5 and 1.8 months respectively (p = 0.041), and efs was 3.0 and 1.7 months respectively (p = 0.009). CONCLUSIONS: In our study cohort, second-line therapy for EGFR wild-type nsclc with tki (compared with docetaxel) was associated with statistically better pfs and efs and noninferior os. Those findings raise the question of whether efs should also be considered when choosing second-line treatment in this patient population.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".