Poster ‐ 22: Retrospective analysis of portal dosimetry based QA of Prostate VMAT Plans
Bibliographic record
Abstract
Purpose: The purpose of this study is to retrospectively analyze the portal dosimetry based quality assurance of prostate VMAT plans. Methods: Our standard quality assurance of VMAT treatment plans are performed using EPID installed on Varian TrueBeam Linac. In our current study we analyzed 84 prostate pretreatment VMAT plans. All plans consisted of two arcs, 7800cGy in 39 fractions with a 6MV beam. For each of these VMAT plans, the measured fluence for each arc is compared with the reference fluence using gamma index analysis. Results: We have compared the gamma passing rates for three criteria; 3%/3mm, 2%/2mm and 1%/1mm. Out of 168 arcs measured, the number below the gamma passing rate 95% using the area, Field+1cm, are 0, 2, and 124 for 3%/3mm, 2%/2mm and 1%/1mm criteria respectively. Corresponding numbers for MLC CIAO are 0, 2, and 139 respectively. The average gamma passing rate for all arcs measured using Field+1cm are 99.9±0.4, 99.6±1.2, and 90.9±6.5 for 3%/3mm, 2%/2mm and 1%/1mm respectively. Similarly if the MLC CIAO area is analyzed, a passing rate of 99.9±0.2, 99.2±1.2 and 87.2±8.5 respectively was observed. The average of the maximum gamma was also found to increase with tighter criteria. Conclusion: Analysis of prostate VMAT quality assurance plans indicate that the gamma passing rate is sensitive to the criteria and the area analyzed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".