Sci—Thur AM: YIS ‐ 05: 10X‐FFF VMAT for Lung SABR: an Investigation of Peripheral Dose
Bibliographic record
Abstract
Flattening Filter Free (FFF) beams exhibit high dose rates, reduced head scatter, leaf transmission and leakage radiation. For VMAT lung SABR, treatment time can be significantly reduced using high dose rate FFF beams while maintaining plan quality and accuracy. Another possible advantage offered by FFF beams for VMAT lung SABR is the reduction in peripheral dose. The focus of this study was to investigate and quantify the reduction of peripheral dose offered by FFF beams for VMAT lung SABR. The peripheral doses delivered by VMAT Lung SABR treatments using FFF and flattened beams were investigated for the Varian Truebeam linac. This study was conducted in three stages, (1): ion chamber measurement of peripheral dose for various plans, (2): validation of AAA, Acuros XB and Monte Carlo for peripheral dose using measured data, and (3): using the validated Monte Carlo model to evaluate peripheral doses for 6 VMAT lung SABR treatments. Three energies, 6X, 10X, and 10X‐FFF were used for all stages. Measured data indicates that 10X‐FFF delivers the lowest peripheral dose of the three energies studied. AAA and Acuros XB dose calculation algorithms were identified as inadequate, and Monte Carlo was validated for accurate peripheral dose prediction. The Monte Carlo‐calculated VMAT lung SABR plans show a significant reduction in peripheral dose for 10X‐FFF plans compared to the standard 6X plans, while no significant reduction was showed when compared to 10X. This reduction combined with shorter treatment time makes 10X‐FFF beams the optimal choice for superior VMAT lung SABR treatments.
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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 | 0.000 | 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) | 0.029 | 0.004 |
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".