Evaluating the Efficacy and Safety of CyberKnife for Meningiomas: A Systematic Review
Bibliographic record
Abstract
This systematic review aims to evaluate CyberKnife (Accuray, Madison, WI, USA) radiosurgery's efficacy, safety, and outcomes in treating meningiomas, focusing on tumour control rates, symptom relief, survival rates, quality of life, and adverse events. A comprehensive literature search was conducted across PubMed, EMBASE, Web of Science, Google Scholar, and Cumulative Index to Nursing and Allied Health Literature (CINAHL), covering studies published in the last 20 years and available in English. The inclusion criteria targeted studies involving patients with meningioma treated with CyberKnife radiosurgery, reporting on specific outcomes of interest. Quality assessment was performed using the Newcastle-Ottawa Scale for observational studies, and a narrative synthesis approach was adopted for data analysis. Twenty-one studies met the inclusion criteria, encompassing various design types and patient demographics. The review highlights CyberKnife's effectiveness in managing benign and atypical meningiomas and specific challenging cases like perioptic lesions and large cranial base tumours. Key findings include high tumour control rates, preservation or improvement of visual functions in perioptic lesions, and promising results in benign spinal tumours and supratentorial meningiomas. Comparative analyses suggest better radiographic tumour control and a lower incidence of post-treatment complications with stereotactic radiotherapy over stereotactic radiosurgery. Long-term outcomes and safety profiles underline the viability of CyberKnife as a treatment option, with minimal permanent side effects reported. CyberKnife radiosurgery is a highly effective and safe treatment modality for meningiomas. It offers significant benefits in tumour control, symptom relief, and maintaining the quality of life with minimal adverse effects. The precision and adaptability of CyberKnife technology make it a valuable addition to the treatment arsenal for meningiomas. It necessitates further research and adoption in clinical practice, especially in regions like the United Arab Emirates, where its use is emerging.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".