Hypofractionated Stereotactic Radiotherapy for the Treatment of Benign Intracranial Meningiomas: Long-Term Safety and Efficacy
Bibliographic record
Abstract
INTRODUCTION: Hypofractionated stereotactic radiotherapy (hSRT) has emerged as an alternative to single-fraction stereotactic radiosurgery (SRS) and conventionally fractionated radiotherapy for the treatment of intracranial meningiomas (ICMs). However, there is a need for data showing long-term efficacy and complication rates, particularly for larger tumors in sensitive locations. METHODS: A retrospective review was conducted on adult patients with ICMs seen at a tertiary care center. Eligible patients were treated with the CyberKnife platform and had a planned treatment course of 3-5 fractions from 2011-2020. The local control was assessed based on radiographic stability and the late toxicity/radionecrosis rates were recorded. Radiographic progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method. RESULTS: In total, 62 patients (age 26-87) with 67 treated tumors were included in this study with a median follow-up of 64.7 months. RT was delivered as the primary treatment in 62.7% of cases and for recurrence in 37.3%. The most common tumor locations were the convexity of the brain and the base of the skull. The tumor sizes ranged from 0.1-51.8 cc and the median planning target volume was 4.9 cc. The most common treatment schedule was 18 Gy in 3 fractions. The five-year PFS and OS were 85.2% and 91.0%, respectively. The late grade III/IV toxicity rate was 3.2% and the radionecrosis rate was 4.8%. CONCLUSIONS: Based on our data, hSRT remains an effective modality to treat low-grade ICMs with acceptable long-term toxicity and radionecrosis rates. hSRT should be offered to patients who are not ideal candidates for SRS while preserving the benefits of hypofractionation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".