Miliary metastases are associated with epidermal growth factor receptor mutations in non-small cell lung cancer: a population-based study
Bibliographic record
Abstract
BACKGROUND: Miliary metastases are characterized by metastatic nodules that are diffuse, innumerable and small. The purpose of this study was to examine the incidence, prognostic significance and impact of epidermal growth factor receptor (EGFR) mutations for miliary metastases from non-small cell lung cancer (NSCLC). MATERIAL AND METHODS: Patients were identified from a Provincial cancer registry (British Columbia, Canada) for the period 2010-2012. Inclusion criteria were stage IV NSCLC at presentation and conclusive EGFR mutation testing. Miliary metastases were defined by subjective and objective radiographic criteria. The primary endpoint was the incidence of miliary lung, brain and liver metastases. Secondary endpoints were survival and the prognostic implication for each site of miliary metastases. RESULTS: For 543 patients, the total number of brain, lung and liver metastases were 165 (30.4%), 290 (53.4%) and 67 (12.3%), respectively. The EGFR mutation positive (EGFR+) subgroup had a significantly higher 3-year cumulative incidence of miliary brain (4.1 vs. 0.5%, p = .015) and miliary lung (11.6 vs. 3.3%, p < .001) metastases compared to EGFR wild type (WT). A greater proportion of metastases from EGFR + cancers were miliary for brain (8.5 vs. 1.7%, p = .035) and lung (18.9 vs. 6.9%, p = .003) sites. Only non-miliary brain (HR = 1.45) and liver (HR = 1.70) metastases predicted for poor overall survival. CONCLUSIONS: Mutations in EGFR were associated with a higher rate of miliary brain and lung metastases. The presence of miliary metastases did not predict for poor overall survival.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".