Neoadjuvant Stereotactic Radiosurgery for Large Brain Metastases: An International, Multicenter, Single-Arm Phase II Trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Previous reports have suggested that neoadjuvant stereotactic radiosurgery (SRS) for brain metastases (BrMets) mitigates the elevated risks of radiation necrosis (RN) and meningeal recurrence associated with adjuvant SRS. We report treatment outcomes from a multicenter phase II trial (NCT03368625) of single-fraction neoadjuvant SRS for large BrMets. METHODS: Patients with 1 index BrMet requiring resection and up to 9 nonindex BrMets not requiring resection were recruited across 3 centers and treated with single-fraction SRS (14-21 Gy) targeting the index lesion with a 2-mm margin, followed by surgical resection. Nonindex lesions were targeted with definitive SRS. The primary end point was 1-year rate of grade 2+ RN affecting the index lesion. Secondary end points included median overall survival, 2-year intracranial progression-free survival, and 1-year rates of local failure (LF) affecting the index lesion, leptomeningeal disease, and pachymeningeal disease. RESULTS: Between April 2018 and November 2022, 35 patients were enrolled; the median follow-up period was 11.8 months (IQR: 6.14, 15.9). No patients developed grade 2+ RN. Six patients experienced LF (1-year rate: 18.0% [95% CI: 7.03, 32.9]); 1 patient developed classic leptomeningeal disease (1-year rate: 2.9% [95% CI: 0.21, 12.9]), and 1 patient developed pachymeningeal disease (1-year rate: 3.2% [95% CI: 0.22, 14.6]). The median overall survival was 13.8 months (95% CI: 8.15, 22.4), and the 2-year intracranial progression-free survival was 29.5% (95% CI: 13.8, 63.1). CONCLUSION: In this study, no patients experienced symptomatic RN and the incidence of meningeal failure was lower than historical rates associated with postoperative SRS. However, the high 1-year rate of LF suggests a potential benefit for higher or fractionated radiation doses or larger clinical target volume margins.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".