Poster — Thur Eve — 30: Comparison of treatment planning and delivery performance of VMAT versus IMRT
Bibliographic record
Abstract
The purpose of this study was to determine whether VMAT (Varian RapidArc ™) treatment planning and delivery performance is in compliance with accepted quality assurance tolerances developed for sliding window IMRT. We present an analysis of data for over 1300 patients treated with VMAT and IMRT over a period of three years. Data was acquired on 6 dosimetrically matched linacs for sites including head and neck, brain, gynaecological, and a variety of other cancer cases treated with 6 MV. We have demonstrated that it is possible to dosimetrically match multiple Varian iX linacs with the millennium series MLC using a sliding gap and intercept test. QA is performed by Monte Carlo simulation and ion chamber measurement comparisons with Varian Eclipse TPS as well as linac log file analysis of MLC positions, gantry angles and monitor units on each patient. Point dose and 3D gamma analysis indicate that agreement between Eclipse and measurement or Monte Carlo calculation is site specific, with the dosimetric leaf gap parameter in Eclipse optimized for the most frequently treated site Point dose agreement within 2% and gamma pass rate of > 95% (3%/ 3 mm) is achievable for all sites for both IMRT and VMAT. Linac log file analysis indicates that planned MLC positions are achieved within 2 mm >99.7% of the time for both sliding window IMRT and VMAT. Planned gantry angles are achieved within 0.6 mm 99.8% of the time and planned MU's within 0.1 mm are achieved 99.8% of the time for VMAT.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".