Abstract 6465: A population-based analysis of brain metastasis burden and management in 8705 small cell lung cancer patients
Bibliographic record
Abstract
Abstract Introduction: Patients with small cell lung cancer (SCLC) have historically been characterized by poor overall survival (OS) and high risk for brain metastases (BM), but large-scale real-world evidence on clinical presentation and treatment in this population is lacking. Methods: The aim of this study was to describe the clinical characteristics and outcomes of patients with SCLC and BM in Ontario, Canada. This retrospective cohort study included all patients in Ontario, Canada, who were diagnosed with SCLC between April 2010 and March 2018. Data were analyzed between June 2022 and July 2023. Kaplan-Meier and multivariable Cox regression analyses were performed to compare overall survival (OS) between patient cohorts stratified by disease stage, BM diagnosis, or first-line intracranial treatment modality. Results: 8705 patients were included. Median age at diagnosis was 68 years (range 18-103). Median OS of all patients was 7.46 months (95% confidence interval, CI, 7.23-7.69). 32% (n=2686) of patients developed BM (synchronous, 43.7%; asynchronous, 56.3%) and had median OS of 9.76 months (95% CI, 9.36-10.22). 102, 1654, and 880 patients received stereotactic radiosurgery (SRS), whole brain radiation therapy (WBRT), and no treatment, respectively, for their BM in the first-line setting or after prophylactic cranial irradiation (PCI). In propensity score-matched analyses, OS was equivocal between SRS- and WBRT-treated cohorts among patients who did not receive PCI (hazard ratio, HR, 1.18, 95% CI, 0.75-1.81) and those who received PCI prior to BM development (HR, 0.77, 95% CI, 0.53-1.13). Conclusions: Survival for patients with SCLC remains poor, and many patients present with BMs. With careful selection, patients with SCLC may benefit from SRS treatment. Citation Format: Karolina Gaebe, Anders W. Erickson, Sarah Chen, Arjun Sahgal, Benjamin H. Lok, Kelvin K. Chan, Sunit Das. A population-based analysis of brain metastasis burden and management in 8705 small cell lung cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6465.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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 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".