Hypofractionation of Gamma Knife Radiosurgery for Intracranial Meningiomas: A Retrospective Multicenter Study and Systematic Review of Literature
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hypofractionated Gamma Knife radiosurgery (hfGKRS) is increasingly considered for treating large or near-critical structure meningiomas because of potential safety advantages. However, data on optimal fractionation and long-term outcomes remain limited. This study evaluated the longer-term tumor control and toxicity after hfGKRS for intracranial meningiomas at 2 large centers, supplemented by a systematic review and meta-analysis of existing literature. METHODS: The analysis included 34 patients (site 1 = 25, site 2 = 9, median age 62.6 years) with 40 tumors (median volume 11.2 cm 3 ). 62% was low-grade (World Health Organization grade 0-1) and 38% was high-grade (World Health Organization grade 2-3). The most common fractionation schemes were 20 Gy in 5 fractions for low-grade and 21 Gy in 3 fractions for high-grade tumors. The mean follow-up was 28.8 months. RESULTS: Only 6 of 34 patients did not have any previous treatment including surgery and/or radiotherapy. 82% of patient patients had neurological deficits before stereotactic radiosurgery. The estimated rate of 5-year tumor progression for low-grade and high-grade tumors was 7.7% (95% CI 0.41%-30%) and 36% (95% CI 12%-62%). Symptoms improved in 12 patients (35%) and worsened in 6 patients (16%), with 1 case attributed to tumor progression and no significant visual deterioration in 16 tumors within 3 mm of the optic apparatus. There was no statistically significant association between fractionation (3 vs 5) scheme and tumor control ( P = .07) or survival ( P = .12). Karnofsky Performance Status performance was a significant predictor of death (HR 0.89, P = .012) and tumor progression (HR 0.93, P = .048). The combined meta-analysis revealed a 5-year tumor control rate of 91.6% for low-grade and 37.9% for high-grade meningiomas. CONCLUSION: hfGKRS demonstrates durable control and acceptable safety for low-grade intracranial meningiomas. High-grade tumors showed less favorable outcomes comparable with single-session Gamma Knife radiosurgery historical data. Further prospective data are needed to confirm these findings and optimize fractionation strategies.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".