Retrospective analysis of portal dosimetry pre-treatment quality assurance of hybrid IMRT breast treatment plans
Bibliographic record
Abstract
Abstract Background: The purpose of this study is to evaluate the effectiveness and sensitivity of the Varian portal dosimetry (PD) system as a quality assurance (QA) tool for breast intensity-modulated radiation therapy (IMRT) treatment plans. Materials and methods: Four hundred portal dose images from 200 breast cancer patient IMRT treatment plans were analysed. The images were obtained using Varian PortalVision electronic portal imaging devices (EPIDs) on Varian TrueBeam Linacs. Three patient plans were selected, and the multi-leaf collimator (MLC) positions were randomly altered by a mean of 0·5, 1, 1·5 and 2 mm with a standard deviation of 0·1 mm on 50, 75 and 100% of control points. Using the improved/global gamma calculation algorithm with a low-dose threshold of 10% in the EPID, the change in gamma passing rates for 3%/3 mm, 2%/2 mm and 1%/1 mm criterion was analysed as a function of the introduced error. The changes in the dose distributions of clinical target volume and organ at risk due to MLC positioning errors were also analysed. Results: Symmetric and asymmetric breast or chest wall plan fields are different in delivery as well as in the QA. An average gamma passing rate of 99·8 ± 0·5 is presented for 3%/3 mm symmetric plans and 96·9 ± 4·5 is presented for 3%/3 mm asymmetric plans. An average gamma passing rate of 98·4 ± 4·3 is presented for 2%/2 mm symmetric plans and 89·7 ± 9·5 is presented for 2%/2 mm asymmetric plans. A large-induced error in MLC positioning (2·0 mm, 100% of control points) results in an insignificant change in dose that would be delivered to the patient. However, EPID portal dosimetry is sensitive enough to detect even the slightest change in MLC positioning error (0·5 mm, 50% of control points). Conclusions: Stricter pre-treatment QA action levels can be established for breast IMRT plans utilising EPID. For improved sensitivity, a multigamma criteria approach is recommended. The PD tool is sensitive enough to detect MLC positioning errors that contribute to even insignificant dose changes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".