A Monte Carlo evaluation of RapidArc dose calculations for oropharynx radiotherapy
Bibliographic record
Abstract
RapidArc, recently released by Varian Medical Systems, is a novel extension of IMRT in which an optimized 3D dose distribution may be delivered in a single gantry rotation of 360 degrees or less. The purpose of this study was to investigate the accuracy of the analytical anisotropic algorithm (AAA), the sole algorithm for photon dose calculations of RapidArc treatment plans. The clinical site chosen was oropharynx and the associated nodes involved. The VIMC-Arc system, which utilizes BEAMnrc and DOSXYZnrc for particle transport through the linac head and patient CT phantom, was used as a benchmarking tool. As part of this study, the dose for a single static aperture, typical for RapidArc delivery, was calculated by the AAA, MC and compared with the film. This film measurement confirmed MC modeling of the beam aperture in water. It also demonstrated that the AAA dosimetric error can be as high as 12% near isolated leaf edges and up to 5% at the leaf end. The composite effect of these errors in a full RapidArc calculation in water involving a C-shaped target and the associated organ at risk produced a 1.5% overprediction of the mean target dose. In our cohort of six patients, the AAA was found, on average, to overestimate the PTV60 coverage at the 95% level in the presence of air cavities by 1.0% (SD = 1.1%). Removing the air cavities from the target volumes reduced these differences by about a factor of 2. The dose to critical structures was also overestimated by the AAA. The mean dose to the spinal cord was higher by 1.8% (SD = 0.8%), while the effective maximum dose (D2%) was only 0.2% higher (SD = 0.6%). The mean dose to the parotid glands was overestimated by approximately 9%. This study has shown that the accuracy of the AAA for RapidArc dose calculations, performed at a resolution of 2.5 mm or better, is adequate for clinical use.
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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 | 0.000 | 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) | 0.000 | 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".