Neurological Death is Common in Patients With EGFR Mutant Non-Small Cell Lung Cancer Diagnosed With Brain Metastases
Bibliographic record
Abstract
Purpose Brain metastases (BrM) are common in patients with epidermal growth factor receptor (EGFRm) mutant non-small cell lung cancer (NSCLC). We sought to determine the rate of neurologic death (ND) in this population. Methods and Materials We analyzed data from 198 patients who received a diagnosis of BrM from EGFRm NSCLC between 2004 and 2016, comparing patients whose initial treatment for BrM was stereotactic radiosurgery with or without tyrosine kinase inhibitors (TKI), whole brain radiation therapy (WBRT) with or without TKI, or TKI alone. The incidence of ND was determined using a competing risks analysis. Univariate and multivariate analyses were used to identify clinical variables associated with this outcome. Results The percentage of patients who initially received stereotactic radiosurgery, whole brain radiation therapy, or TKI alone was 22%, 61%, and 17%, respectively. Median overall survival in these subgroups was 31.1, 14.6, and 24.6 months, respectively ( P = .0016). The 5-year incidence of ND among all patients was 40% and did not significantly vary according to treatment group. In a multivariable model, only leptomeningeal disease at any point in a patient's disease course significantly correlated with ND (hazard ratio 4.75, P <.001). Conclusions Among our cohort of patients with BrM from EGFRm NSCLC, the incidence of ND was significantly higher than suggested by previous reports. BrM should be considered a driver of mortality in many patients with EGFRm NSCLC, and treatments providing better control of BrM, lower neurocognitive side effects, and maintenance of quality of life are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".