Rebiopsy Enhances Survival with Afatinib vs. Osimertinib in EGFR Exon 19 Deletion Non-Small Cell Lung Cancer: A Multicenter Study in Taiwan
Bibliographic record
Abstract
BACKGROUND: Afatinib and Osimertinib are first-line treatments for EGFR-mutated advanced non-small cell lung cancer (NSCLC), but their comparative efficacies and the patient groups that benefit the most remain unclear. This multicenter retrospective study evaluated the efficacy of first-line Afatinib and Osimertinib in NSCLC patients with EGFR 19del and no brain metastases at diagnosis. METHODS: The primary endpoints were time on treatment (ToT) and overall survival (OS). Survival analyses were performed for three groups: Afatinib followed by Osimertinib, Afatinib followed by other therapies, and Osimertinib (alone or followed by other therapies). Rebiopsy practices, including T790M mutation detection, were also analyzed in patients with disease progression on Afatinib. RESULTS: = 0.473). Osimertinib demonstrated advantages, with fewer brain metastases upon progression and fewer adverse effects. In the Afatinib group, 64% of patients with disease progression underwent rebiopsy, with 39% testing positive for T790M mutation and subsequently receiving Osimertinib. Rebiopsy was most frequently performed on the lung parenchyma using non-surgical methods. CONCLUSIONS: In this real-world study, Osimertinib achieved a significantly longer ToT compared to Afatinib in NSCLC patients with EGFR 19del and no brain metastases. The sequential use of Afatinib followed by Osimertinib showed a trend toward improved OS, highlighting the importance of rebiopsy for identifying T790M mutations to guide subsequent therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".