Effect of brain metastases on survival and systemic treatment of EGFR/ALK-driven non-small cell lung cancer (NSCLC).
Bibliographic record
Abstract
e20527 Background: Survival with EGFR/ALK+ve NSCLC can be prolonged with tyrosine kinase inhibitors (TKIs), but brain metastases (mets) are common. TKIs may control brain mets poorly, and radiation may have neurocognitive toxicity. Deteriorating performance status (PS) due to brain mets may affect the number of lines of treatment. We aimed to evaluate the impact of brain mets on lines of systemic therapy and overall survival (OS). Methods: This retrospective analysis included patients with EGFR/ALK+ve NSCLC treated at Princess Margaret Cancer Centre from 1998-2015. 2 groups were analyzed: those with brain mets at diagnosis of stage IV disease or on 1st line therapy, and those without brain mets. OS was calculated from date of metastatic disease using Kaplan-Meier method, and differences between groups were tested with the log-rank test. Results: 291 patients were included: 141 with and 150 without brain mets. Summary results are shown in the Table. 106/141 had brain mets at diagnosis of stage IV NSCLC. There were more patients with relapsed early stage NSCLC in the no brain mets group (29% vs 19%), and more smokers in the brain mets group (18% vs 7%). 1st treatment for brain mets included WBRT (84), SRS (32) and TKI alone (24). 1st line systemic therapy was TKI in 83% of brain mets group and 61% of no brain mets group. Median OS was longer in the no brain mets group: 4 vs 2.1 years, HR 1.55, p=0.013. There was no observed difference in number of treatment lines: median 1 in both groups, p=0.53. Conclusions: Patients with brain mets from EGFR/ALK+ve NSCLC have inferior OS, despite TKIs and similar exposure to systemic therapy. Further focus on this group is necessary to improve outcomes. No Brain mets N=150 Brain mets N=141 P Age Median (Range) 61.9(27.6- 82.9) 59.8(29.2- 86.3) 0.35 Female 98(65%) 96(68%) 0.71 Smoking History No 139(93%) 115(82%) 0.005 Yes 11(7%) 26(18%) Ethnicity Asian 72(48%) 63(45%) 0.85 Caucasian 55(37%) 56(40%) Other 23(15%) 22(15%) ECOG PS 0-1 141(94%) 133(94%) 0.72 2-3 9(6%) 8(6%) Stage IV at First Dx 106(71%) 114(81%) 0.056 Mutation ALK 21(14%) 14(10%) 0.37 EGFR 129(86%) 127(90%) Lines of treatment Median(range) 1(0-9) 1(0-4) 0.53 Median Survival Years 4.0 2.1 0.011 HR 1.55 (95%CI 1.1-2.2)
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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 | 0.001 |
| 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".