Role of Gamma Knife Radiosurgery in Small Cell Lung Cancer: A Multi-Institutional Retrospective Study of the International Radiosurgery Research Foundation (IRRF)
Bibliographic record
Abstract
BACKGROUND: Despite a high incidence of brain metastases in patients with small-cell lung cancer (SCLC), limited data exist on the use of stereotactic radiosurgery (SRS), specifically Gamma Knife™ radiosurgery (Elekta AB), for SCLC brain metastases. OBJECTIVE: To provide a detailed analysis of SCLC patients treated with SRS, focusing on local failure, distant brain failure, and overall survival (OS). METHODS: A multi-institutional retrospective review was performed on 293 patients undergoing SRS for SCLC brain metastases at 10 medical centers from 1991 to 2017. Data collection was performed according to individual institutional review boards, and analyses were performed using binary logistic regression, Cox-proportional hazard models, Kaplan-Meier survival analysis, and competing risks analysis. RESULTS: Two hundred thirty-two (79%) patients received SRS as salvage following prior whole-brain irradiation (WBRT) or prophylactic cranial irradiation, with a median marginal dose of 18 Gy. At median follow-up after SRS of 6.4 and 18.0 mo for surviving patients, the 1-yr local failure, distant brain failure, and OS were 31%, 49%, and 28%. The interval between WBRT and SRS was predictive of improved OS for patients receiving SRS more than 1 yr after initial treatment (21%, <1 yr vs 36%, >1 yr, P = .01). On multivariate analysis, older age was the only significant predictor for OS (hazard ratio 1.63, 95% CI 1.16-2.29, P = .005). CONCLUSION: SRS plays an important role in the management of brain metastases from SCLC, especially in salvage therapy following WBRT. Ongoing prospective trials will better assess the value of radiosurgery in the primary management of SCLC brain metastases and potentially challenge the standard application of WBRT in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".