CyberKnife Radiosurgery of Skull-base Tumors: A UK Center Experience
Bibliographic record
Abstract
The study aim was to evaluate patient individualized Cyberknife® treatment for heterogeneous skull-base tumors. Patients treated between 2009 and 2013 at The Harley Street Clinic were studied. In total, 66 patients received 15-30 Gy in 1-5 fractions to a median planning target volume (PTV) of 6.4 cc, including patients with secondary, multiple, residual and recurrent tumors, and those with tumors of uncertain pathological type. Outcome analysis was pragmatically restricted to 35 patients who had single, primary tumors treated with curative intent, and sufficient diagnostic and outcome information. Sixteen vestibular schwannoma patients with median PTV 3.8 cc (range 0.81-19.6) received 18-25 Gy in 3-5 fractions: 81% showed no acute toxicity, 50% reported no late toxicity, 71% of symptoms were stable/improved and local control was 100% at 11.4 months median follow-up. Twelve meningioma patients with median PTV of 5.5 cc (range 0.68-22.3) received 17-30 Gy in 1-5 fractions: 83% experienced no acute toxicity, 33% reported no late toxicity, 88% of symptoms were stable/improved and local control was 100% at 22.1 months median follow-up. Seven patients with other tumor types with median PTV of 24.3 cc (range 7.6-100.5) received 15-28.5 Gy in 1-5 fractions: 57% experienced no acute toxicity, 57% reported no late toxicities, 66% of symptoms were stable and local control was 43% at 14.9 months median follow-up. When tumor types were considered together, smaller tumors (PTV < 6.4 cc) showed reduced acute toxicity (p = 0.01). Overall, smaller benign tumors showed low acute toxicity, excellent local control, and good symptom management: a focus on enhanced neurological preservation may refine outcomes. For other tumor types outcome was encouraging: a focus on optimal dose and fractionation scheduling may reduce toxicity and improve local control. Individual patient experiences are detailed where valuable lessons were gained for optimizing local control and minimizing toxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".