DDRE-04. EFFECTIVENESS AND SAFETY OF OSIMERTINIB FOR PATIENTS WITH LEPTOMENINGEAL METASTASES FORM NON-SMALL CELL LUNG CANCER: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract BACKGROUND the FLAURA study established Osimertinib to be the priority choice for the treatment of metastatic non-small cell lung cancer (NSCLC) harboring mutated epidermal growth factor receptor (EGFR). However, like many previous studies, the FLAURA study excluded patients with symptomatic CNS metastases. Hence, we performed a systematic review and meta-analyses of studies to assess the efficiency and safety of Osimertinib for the treatment of NSCLC with leptomeningeal metastases (LM). METHODS We included studies published between 2010 and 2021 that evaluated the efficacy and toxicity of Osimertinib in NSCLC patients with LM. We searched PubMed, Embase, and oncology meeting abstracts (ASCO, ESMO and WCLC). Primary outcomes were objective response rate (ORR), disease control rate (DCR), any grade 3/4 toxicity rate. We used the random-effects model to generate pooled estimates for proportions. Newcastle-Ottawa Scale (NOS) was used to assess the certainty in the evidence. RESULTS Twelve studies reporting on 367 patients were included in the meta-analysis. Most patients (≥90%) received Osimertinib as at least second line of treatment. The objective response rate was 42% (95% CI, 24%-59%; n = 184), and CNS disease control rate was 90% (95% CI, 85%-94%; n = 154). Intracranial progression free survival and overall survival ranged from 3.7 to 15.6 months, and 11.0 to 18.8 months, respectively. Adverse events were similar with previous studies and Common Terminology Criteria for Adverse Events (version 3.0) grade 3 or higher adverse event rates was acceptable. CONCLUSION This meta-analysis confirmed that for advanced NSCLC with LM harboring EGFR-TKI-sensitizing mutations, Osimertinib showed impressive antitumor activity and acceptable toxicity.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".