Stereotactic radiosurgery (SRS) versus whole brain radiation therapy (WBRT) in patients with small cell lung cancer (SCLC) and intracranial metastatic disease (IMD): A systematic review and meta-analysis.
Bibliographic record
Abstract
8570 Background: Patients with SCLC are at high risk for the development of IMD and, subsequently, rapid intracranial progression. SRS has supplanted WBRT as first-line treatment for IMD in most solid cancers, but WBRT remains first-line treatment for IMD in SCLC patients. Data on SRS in SCLC are limited to small retrospective studies. Methods: Studies reporting on SRS in SCLC patients with IMD were collected from EMBASE, MEDLINE, CENTRAL, and grey literature sources (n = 3,732 studies). Random-effects meta-analysis pooled hazard ratios (HR) for overall survival (OS) between SRS and WBRT ± SRS boost, as well as medians for OS in months (mo) and risk rates for intracranial local (LC) and intracranial distant control (DC) in single-arm SRS studies. Results: OS following SRS was non-inferior compared with WBRT ± SRS boost (HR 0.90; 95% confidence interval (95CI), 0.73-1.10; n = 7 studies; n = 18,130 patients), and superior compared with WBRT alone (HR 0.80; 95CI, 0.66-0.96; n = 7 studies; n = 16,961 patients). Pooled median OS from single-arm studies following SRS was 8.99 mo (95CI, 7.86-10.15; n = 14 studies; n = 1,682 patients). Pooled LC and DC estimates following SRS were 81% (95CI, 67%-99%) and 66% (95CI, 50%- 86%), respectively, at 6 mo, and 78% (95CI, 61%-98%) and 58% (95CI, 46%-75%), respectively, at 12 mo. Conclusions: This systematic review and meta-analysis provides evidence that SRS may achieve analogous survival outcomes compared with WBRT in patients with SCLC and IMD, indicating that a subset of SCLC patients may benefit from first-line SRS treatment. Prospective trials should investigate the impact of metastatic burden as well as LC and DC differences between WBRT- and SRS-treated SCLC patients.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".