An individual patient-level meta-analysis of non–small-cell lung cancer leptomeningeal metastases treated with epidermal growth factor receptor inhibitors.
Bibliographic record
Abstract
e21118 Background: Leptomeningeal metastasis (LM) is a debilitating condition associated with advanced non-small cell lung cancer (NSCLC). When driver mutations in the epidermal growth factor receptor (EGFR) are identified, targeted therapies (TT) represent an appealing therapeutic strategy. However, the efficacy of TT for LM is unknown as LM patients are routinely omitted from clinical trials. Methods: We conducted a systematic review and meta-analysis of individual patient data to evaluate the efficacy of EGFR TT in EGFR-mutant NSCLC LM in accordance with PRISMA guidelines. EGFR-TTs evaluated were erlotinib, gefitinib, icotinib, afatinib, dacomitinib, osimertinib, zorifertinib, and furmonertinib. The co-primary endpoints were progression-free survival (PFS) and overall survival (OS). To assess differences between groups, shared frailty Cox regression models were used to estimate the hazard ratio (HR), 95% confidence interval (CI) and p-value. Results: 7780 abstracts were screened, identifying 126 publications with 232 patients, corresponding to 272 lines of EGFR-TT for NSCLC LM which met inclusion criteria. The overall median PFS (mPFS) in patients receiving any EGFR-TT was 9.15 months and the median overall survival was 14.5 months. In univariable analyses, osimertinib was associated prolonged PFS compared to other EGFR-TT, (mPFS of 11 versus 8 months, HR = 0.64; 95% CI: 0.44-0.93; P = 0.02). Osimertinib was numerically associated with prolonged OS compared to other EGFR-TT (mOS 16.8 months versus 13.6 months, HR = 0.72; 95% CI 0.44-1.17, P = 0.188). In multivariable analyses, ECOG performance status > 2 was independently associated with shortened PFS (mPFS 10 versus 8 months, HR = 1.54, 95% CI: 1.26-3.04; P < 0.05) and OS (mOS 16.5 versus 10 months, HR = 3.33, 95% CI: 1.80-6.18; P < 0.05). Conclusions: In the largest cohort of NSCLC LM treated with EGFR TT compiled to date, osimertinib is associated with marginally improved outcomes compared to other EGFR TTs. ECOG performance status is an independent predictor of prognosis in these patients. These results highlight the need for prospective studies for this difficult to treat 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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.052 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".