Lung cancer epidermal growth factor receptor mutations and radiotherapy response: A multicentre clinical study
Bibliographic record
Abstract
PURPOSE: To examine the impact of epidermal growth factor receptor (EGFR) mutations on objective response to palliative lung radiotherapy in patients with metastatic non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: A multicentre retrospective study was conducted of patients with metastatic NSCLC diagnosed between March 2010 and June 2012 who received palliative radiotherapy to the chest. Patients included for study had baseline imaging and follow-up imaging 1-3 months after radiotherapy. The primary endpoint was 1-3 month local objective imaging response by the Response Evaluation Criteria in Solid Tumours (RECIST). Patients were divided into EGFR mutation positive (EGFR+) and EGFR wild type (WT) cohorts for analysis. RESULTS: There were 121 patients for study inclusion: 89 (74%) were EGFR WT and 32 (26%) were EGFR+. The response rate between EGFR WT and EGFR+ cohorts was not significantly different (49 vs. 63%, p = 0.21). On multivariate analysis, initiation of a tyrosine kinase inhibitor (TKI) after radiotherapy was associated with a higher rate of response (OR: 5.07, 95%CI: 1.08-23.69, p = 0.039) but EGFR mutation status was not. For the EGFR+ cohort, patients with disease progression after initial management on a TKI had a worse response rate compared to patients who were TKI-naïve before starting radiotherapy (30 vs. 77%, p = 0.018). Local control was not statistically different between the EGFR cohorts. CONCLUSION: The EGFR mutation status alone was not an independent predictor of objective radiographic response to palliative thoracic radiotherapy. Acquired resistance to TKI therapy may be associated with disease cross-resistance to palliative radiotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".