Osimertinib for EGFR-mutant lung cancer with central nervous system metastases: a meta-analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: Osimertinib, a third-generation tyrosine kinase inhibitor (TKI), is the only Food and Drug Administration-approved third-generation epidermal growth factor receptor (EGFR)TKI. Osimertinib is a cancer medicine that interferes with the growth and spread of cancer cells in the body. Osimertinib is used to treat a certain type of non-small cell lung cancer. We review some of the main challenges in targeting EGFR, including lack of central nervous system penetration with most tyrosine kinase inhibitors, activity of osimertinib penetrating blood-brain barrier and the efficacy of osimertinib. METHODS: Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conducted a systematic literature search in the databases PubMed, EMBASE, ISI Web of Science database on January 30, 2020, searching for studies investigating the osimertinib efficacy on patients with CNS metastases in EGFR-mutant non-small cell lung cancer (NSCLC). And Newcastle-Ottawa Scale (NOS) was used to assess the certainty in the evidence. RESULTS: The pooled results showed that the overall response rate (ORR) and disease control rate (DCR) were 70% and 92%, respectively, in patients with T790M mutations. The efficacy of osimertinib was confirmed by the median progression free survival (PFS). In untreated advanced EGFR-mutated NSCLC with CNS metastases patients, the pooled ORR and DCR of osimertinib were 71% and 93%, respectively. And the combined median PFS, achieved by osimertinib, was 12.21 months. Above data proved that osimertinib has well activity in disease control, especially in first line. CONCLUSIONS: This meta-analysis confirmed that in treatment-naive advanced NSCLC CNS metastases harboring EGFR-TKI-sensitizing mutations, Osimertinib showed impressive antitumor activity.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".