Factors Influencing the Outcome of Stereotactic Radiosurgery in Patients With Five or More Brain Metastases
Bibliographic record
Abstract
Background: Stereotactic radiosurgery (SRS) for patients with 5 or more brain metastases (BMets) is a matter of debate. We report our results with that approach and the factors influencing outcome. Methods: In the 103 patients who underwent SRS for the treatment of 5 or more BMets, primary histology was non-small-cell lung cancer (57% of patients). All patients were grouped by Karnofsky performance status and recursive partitioning analysis (RPA) classification. In our cohort, 72% of patients had uncontrolled extracranial disease, and 28% had stable or responding systemic disease. Previous irradiation for 1–4 BMets had been given to 56 patients (54%). The mean number of treated BMets was 7 (range: 5–19), and the median cumulative BMets volume was 2 cm3 (range: 0.06–28 cm3). Results: Multivariate analyses showed that stable extracranial disease (p < 0.001) and RPA (p = 0.022) were independent prognostic factors for overall survival (OS). Moreover, a cumulative treated BMets volume of less than 6 cm3 (adjusted hazard ratio: 2.54; p = 0.006; 95% confidence interval: 1.30 to 4.99) was associated with better OS. The total number of BMets had no effect on survival (p = 0.206). No variable was found to be predictive of local control. The RPA was significant (p = 0.027) in terms of distant recurrence. Conclusions: Our study suggests that SRS is a reasonable option for the management of patients with 5 or more BMets, especially with a cumulative treatment volume of less than 6 cm3.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".