Volume staging for arteriovenous malformation SRS treatment using VMAT
Bibliographic record
Abstract
Abstract Volume staging involves dividing the target volume into smaller parts and treating each part separately. In this study, the feasibility of volume‐staged stereotactic radiosurgery (VS‐SRS) on a linear accelerator using volumetric modulated arc therapy (VMAT) and a frameless patient positioning system is investigated. Ten patients, previously treated with hypofractionated stereotactic radiotherapy with arteriovenous malformation (AVM) sized from 1.6 to 4.0 cm in diameter, were selected. VS‐SRS plans were created with the VMAT technique on the Varian Eclipse treatment planning system (TPS) using the TrueBeam STx linear accelerator. For each patient, an AVM‐VMAT set was planned with the AVM as the target and a PTV‐VMAT set using the (PTV = AVM+1 mm) as the target. All targets were divided into two sub‐volumes. The TPS data from the AVM‐VMAT plans was compared to Gamma Knife (GK) VS‐SRS plan data available in the literature. The AVM‐VMAT and PTV‐VMAT plans were compared to investigate the effect of a 1 mm PTV margin on normal brain (NB) dose. End‐to‐end testing was performed using a GaFchromic EBT3 film and point‐dose measurements. Dosimetric effects of multiple setups were investigated through film‐to‐film comparisons. Median target dose coverage, NB V 12Gy , and conformity index for the AVM‐VMAT plans were 97.5%, 17 cm 3 , and 0.8, respectively. PTV‐VMAT plans attained comparable target dose coverage, but the average NB V 12Gy increased by 48.9% when compared to the AVM‐VMAT plans. Agreement of point‐dose measurements with TPS calculations was −0.6% when averaged over all patients. Gamma analysis passing rates were above 90% for all film‐to‐film comparisons (2%/1 mm criteria), and for the film to TPS comparison (5%/1 mm). This work suggests that VMAT is capable of producing VS‐SRS plans with similar dose falloff characteristics as GK plans. NB dose depends on PTV margin size, and two‐stage treatment setups do not appear to contribute additional uncertainty to treatment delivery.
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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 | 0.001 | 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) | 0.000 | 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".