Retrospective Practice Review of Treatment of Metastatic Non-Small-Cell Lung Cancer with Second-Line Erlotinib
Bibliographic record
Abstract
BACKGROUND: Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIS) and chemotherapy have both demonstrated efficacy in recurrent metastatic non-small-cell lung cancer (NSCLC) following failure of first-line platinum-based chemotherapy. Although the 3 available therapeutic agents-docetaxel, erlotinib, and pemetrexed-have significantly changed the treatment landscape for recurrent NSCLC, the optimal selection of second- and third-line therapy has not been established. This practice review examines the outcomes in clinical practice of using second-line erlotinib followed by third-line chemotherapy in the treatment of recurrent metastatic NSCLC. METHODS: We conducted a retrospective review of nsclc patient charts at three Canadian institutions. Patients with recurrent nsclc who had received second-line erlotinib therapy followed by third-line chemotherapy were selected by census. A chart review assessed key outcomes that included time to progression (TTP), response, and change in performance status. Outcomes for specific patient subgroups were also examined. RESULTS: We identified 35 patients for this retrospective practice review. First-line platinum-doublet therapy demonstrated a mean TTP of 6.6 months and a 46% overall response rate (15 partial responses and 1 complete response). Second-line treatment with erlotinib produced the highest mean TTP of all lines of therapy (9.2 months) and an overall response rate of 40% (all being partial responses). In the third-line setting, in which most patients received docetaxel, the mean ttp was 4.3 months and the overall response rate was 18% (all being partial responses). Subgroup analysis showed that all patient subgroups demonstrated benefit from second-line erlotinib treatment; improved benefit was observed in patients who developed rash, in female patients, in never smokers, in Asian patients, in patients with positive EGFR status, and in patients with adenocarcinoma histology. CONCLUSIONS: For patients with advanced nsclc who progressed following first-line platinum-based chemotherapy, the data demonstrate that second-line EGFR-TKI treatment is efficacious and well-tolerated and that it does not appear to diminish the benefit of third-line chemotherapy.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".