Radiosurgery for mesial temporal lobe epilepsy following ROSE trial guidelines — A planning comparison between Gamma Knife, Eclipse, and Brainlab
Bibliographic record
Abstract
PURPOSE: This study aims to compare stereotactic radiosurgery (SRS) planning of epilepsy that complies with Radiosurgery or Open Surgery for Epilepsy (ROSE) guidelines in GammaKnife, non-coplanar conformal (NCC) plan in Eclipse, dynamic conformal arc (DCA) plan in Brainlab, and a volumetric modulated arc therapy (VMAT) plan in Eclipse. METHODS: Twenty plans targeting Mesial temporal lobe epilepsy (MTLE) was generated using GammaKnife, Eclipse with 20 NCC beams, Brainlab with 5 DCA, and Eclipse VMAT with 4 arcs observing ROSE trial guidelines. Multivariate analysis of variance and Wilcoxon signed-rank test were used to compare dosimetric data of the plans and perform pairwise comparison, respectively. RESULTS: The plans obeyed the recommended prescription isodose volume (PIV) within 5.5-7.5 cc and maximum doses to brainstem, optic apparatus (OA) of 10 and 8 Gy, respectively, for a prescription dose of 24 Gy. The volumes of the target were in the range 4.0-7.4 cc. Mean PIV, maximum dose to brainstem, OA were 6.5 cc, 10 Gy, 7.9 Gy in GammaKnife; 7.2 cc, 6.1 Gy, 4.5 Gy in Eclipse NCC; 7.2 cc, 6.4 Gy, 5.7 Gy in Brainlab DCA; and 5.2 cc, 8.4 Gy, 6.1 Gy in Eclipse VMAT plans, respectively. Multivariate analysis of variance showed significant differences among the 4 SRS planning techniques (P-values < 0.01). CONCLUSIONS: Among the 4 SRS planning methods, VMAT with least PIV and acceptable maximum doses to brainstem and OA showed highest compliance with ROSE trial. Having the most conformal dose distribution and least dose inhomogeneity, VMAT scored higher than GK, Eclipse NCC, and Brainlab DCA plans.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".