Unraveling Survival Determinants in Patients with Advanced Non-Small-Cell Lung Cancer with EGFR Exon 20 Insertions
Bibliographic record
Abstract
Background: Lung cancer is the leading cause of cancer-related death in Taiwan. It is often associated with mutations in the epidermal growth factor receptor (EGFR) gene, with common mutations accounting for approximately 85% of all EGFR-related cases. However, the remaining 15% are caused by uncommon mutations in EGFR, mainly insertions in exon 20 (about 4%). The response to EGFR tyrosine kinase inhibitors (TKIs) can vary markedly with exon 20 insertions. However, few prior large-scale studies have examined patients with these EGFR mutations. Methods: This study combines the databases of several large hospitals in Taiwan to analyze the effects and clinical significance of rare EGFR mutations on responses to EGFR-TKIs, considering the changes in medication. Results: This study enrolled 38 patients with non-small-cell lung cancer and EGFR exon 20 insertions. It assessed the correlations of various predictors with progression-free survival (PFS) and overall survival (OS). It showed that among those with EGFR exon 20 insertions, the median PFS was 5.15 months, and OS reached 13 months. The median PFS was 5.4 months for afatinib, 5.7 months for chemotherapy, and 4.3 months for first-generation EGFR-TKIs. Conclusions: EGFR-TKIs may be considered as an alternative treatment option for patients with EGFR exon 20 insertions in cases where the currently recommended therapies, such as chemotherapy with or without amivantamab, are either unavailable or intolerable. The potential use of afatinib for specific patients in this context depends on the precise characteristics of their mutation and remains to be determined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".