Survival Benefits of Radiotherapy and Surgery in Lung Cancer Brain Metastases with Poor Prognosis Factors
Bibliographic record
Abstract
BACKGROUND: Radiotherapy and surgery are the standard local treatments for lung cancer brain metastases (BMs). However, limited studies focused on the effects of radiotherapy and surgery in lung cancer BMs with poor prognosis factors. METHODS: We retrospectively analyzed 714 patients with lung cancer BMs. Analyses of overall survival (OS) and risk factors for OS were assessed by the log-rank test and Cox proportional hazard model. RESULTS: Age ≥ 65 years, a Karnofsky Performance Scale (KPS) score ≤ 70, anaplastic large-cell lymphoma kinase (ALK)/epidermal growth factor receptor (EGFR) wild type, and extracranial metastases were related to poor prognosis. Patients were stratified according to these poor prognosis factors. In patients with the ALK/EGFR wild type, whole brain radiotherapy (WBRT), stereotactic radiosurgery (SRS), and surgery improved the OS of patients. WBRT and SRS were the independent protective factors for OS. In patients with extracranial metastases, patients who received WBRT plus SRS or WBRT alone had longer OS than those who did not receive radiotherapy. WBRT plus SRS and WBRT were the independent protective factors for OS. CONCLUSIONS: Radiotherapy and surgery are associated with improved survival for lung cancer BMs with the ALK/EGFR wild type. Radiotherapy is associated with improved survival in lung cancer BMs with extracranial metastases.
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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 | 0.000 | 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) | 0.002 | 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".