P.140 Radiological characteristics of brain metastases in non-small cell lung cancer relative to EGFR mutation status
Bibliographic record
Abstract
Background: Approximately 20-40% of patients with non-small cell lung cancer (NSCLC) will develop brain metastases (BM). The aim of this study was to investigate if Epidermal Growth Factor Receptor (EGFR) status of NSCLC alters the radiological appearances of BM. Also to compare differences in imaging features of BM occurring from EGFR-mutated NSCLC during treatment with Tyrosine Kinase Inhibitors (TKI) versus prior to treatment. Methods: A retrospective study was performed over a 5 year period of all patients with histologically proven NSCLC with BM and known EGFR status. 72 patients met the inclusion criteria. Radiological features were reviewed as well as number, size and location of BM. Results: 18/72 patients had EGFR-mutated NSCLC and of these 9 presented with BM while on TKI treatment. Patients with EGFR-mutated NSCLC had statistically significant higher occurrence of multiple BM (p=0.029) and BM in a central location (p=0.027). BM that occurred during TKI treatment appeared smaller and with minimal surrounding oedema. Conclusions: Given the propensity for multiple BM in EGFR-mutated NSCLC, vigilant imaging follow up would need to be considered. BM presenting while on TKI were more subtle, especially on Computed Tomography (CT), therefore careful follow up with Magnetic Resonance Imaging (MRI) may be required.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".