Patterns of Spread and Prognostic Implications of Lung Cancer Metastasis in an Era of Driver Mutations
Bibliographic record
Abstract
Background: In the present study, we examined the pattern of metastatic spread in patients with advanced non-small-cell lung cancer (NSCLC) and the effect of EGFR mutations. Methods: Patients were identified from a provincial cancer registry, and individual medical records were reviewed. Patients were included if they had stage IV NSCLC and underwent diagnostic EGFR mutation testing. Patients were divided into EGFR mutation-positive (EGFR+) and EGFR wild type (WT) cohorts. The primary endpoint was the cumulative incidence for each metastatic site: lung, bone, brain, liver, adrenal glands, distant nodes, and other. Cumulative incidence curves were estimated using a competing-risks method. The secondary outcome was survival. Results: Of the 543 identified patients, 121 (22.3%) tested as EGFR+, and 422 (77.7%) tested as EGFR WT. The incidence of brain (39.2% vs. 28.2%, p = 0.038) and lung (61.2% vs. 51.0%, p = 0.048) metastasis was higher in the EGFR+ cohort than in the EGFR WT cohort. In the EGFR+ cohort, a higher incidence of liver metastasis was associated with the exon 21 mutation subtype than with the exon 19 deletion subtype [23% vs. 7%, p < 0.01; hazard ratio (HR): 3.47]. Median survival was significantly longer for the EGFR+ cohort than for the EGFR WT cohort (22.4 months vs. 7.9 months, p < 0.001). In multivariable analysis, brain (HR: 1.73), liver (HR: 1.69), and bone (HR: 1.89) metastases were associated with worse survival. Conclusions: Rates of lung and brain metastases are higher in EGFR mutation carriers, even when adjusted for differences in survival. Brain, liver, and bone metastases are independent negative prognostic factors for survival.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".