Comparison of gamma analysis with using different dosimetric systems for pre-treatment verification of intensity-modulated radiation therapy
Bibliographic record
Abstract
The evaluation of the agreement between calculated and measured dose plays an essential role in the quality assurance procedures of intensity-modulated radiation therapy (IMRT). This study aimed to compare gamma analysis using portal dosimetry (PD), Epiqa, and 2D array detector for dose verification of radiotherapy treatment plans. Five field step-and-shoot IMRT plan was performed for 20 prostate IMRT patients using the dual-energy DHX linear accelerator (Varian Medical System, Palo Alto, Calif., USA). The treatment plans were created using Varian DHX Eclipse treatment planning system (TPS) version 15.1. All measurements were performed by aS500 EPID integrated into Varian DHX linear accelerator and 2D array detector. The dose distribution was evaluated with gamma area histograms (GAHs) generated using different γ criteria (1%/1 mm, 2%/2 mm, 3%/2 mm, and 3%/3 mm) for dose agreement and distance to agreement parameters. Statistical analyses were evaluated by using Mann–Whitney U test and Kruskal–Wallis test, and p-value of p < 0.01 was considered to be significant. The average pass rate for 20 IMRT plans was above 95 for all devices with 2%/2 mm, 3%/2 mm, and 3%/3 mm. The mean and standard deviation pass rates (γ ≤ 1) were found to be 99.80 ± 0.19, 99.35 ± 0.34, and 97.53 ± 0.71 for PD, Epiqa, and 2D array, respectively. All IMRT plans passed 2%/2 mm, 3%/2 mm, and 3%/3 mm gamma by more than 95 of three dosimetric systems. They are all in good agreement with the literature. All three devices are acceptable for quality control of IMRT. Due to the simplicity and fast evaluation process, PD can be preferred for quality control.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".