Osimertinib is associated with improved outcomes in pre-treated non-small cell lung cancer leptomeningeal metastases: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE Leptomeningeal metastasis (LM) is a severe complication of non-small cell lung cancer (NSCLC). In patients with NSCLC LM harboring epidermal growth factor receptor ( EGFR ) mutations, osimertinib is favored over alternative EGFR tyrosine kinase inhibitors (TKIs). However, the efficacy of osimertinib relative to other EGFR-TKIs is not well established for patients with LM. We aimed to compare the efficacy of EGFR-TKIs in EGFR- mutated NSCLC LM. METHODS This systematic review and meta-analysis performed according to PRISMA guidelines included studies of adult patients with EGFR -mutated NSCLC and a diagnosis of LM who received an EGFR-TKI for the treatment of LM. We searched Medline ALL, Embase, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science Core Collection. The evaluation of biases was done by using the Ottawa-Newscastle scale. The hazard ratio was used as the parameter of interest for overall survival (OS) and central nervous system-specific progression-free survival (PFS). RESULTS 128 publications were included with 243 patients and 282 lines of EGFR-TKI for NSCLC LM that met inclusion criteria. The median PFS in patients receiving any EGFR-TKI was 9.1 months, and the median OS was 14.5 months. In univariate analyses of the entire cohort, osimertinib treatment demonstrated significantly prolonged PFS, but not OS, compared to other EGFR-TKIs. Osimertinib demonstrated significantly prolonged PFS and OS in the subset of patients who were previously treated with EGFR-TKIs, but not in EGFR-TKI naïve patients. CONCLUSION Osimertinib is associated with improved outcomes compared to other EGFR-TKIs, particularly in patients previously treated with EGFR-TKIs. An important limitation is that most patients were derived from retrospective reports. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".