The accuracy of the out-of-field dose calculations using a model based algorithm in a commercial treatment planning system
Bibliographic record
Abstract
The out-of-field dose can be clinically important as it relates to the dose of the organ-at-risk, although the accuracy of its calculation in commercial radiotherapy treatment planning systems (TPSs) receives less attention. This study evaluates the uncertainties of out-of-field dose calculated with a model based dose calculation algorithm, anisotropic analytical algorithm (AAA), implemented in a commercial radiotherapy TPS, Varian Eclipse V10, by using Monte Carlo (MC) simulations, in which the entire accelerator head is modeled including the multi-leaf collimators. The MC calculated out-of-field doses were validated by experimental measurements. The dose calculations were performed in a water phantom as well as CT based patient geometries and both static and highly modulated intensity-modulated radiation therapy (IMRT) fields were evaluated. We compared the calculated out-of-field doses, defined as lower than 5% of the prescription dose, in four H&N cancer patients and two lung cancer patients treated with volumetric modulated arc therapy (VMAT) and IMRT techniques. The results show that the discrepancy of calculated out-of-field dose profiles between AAA and the MC depends on the depth and is generally less than 1% for in water phantom comparisons and in CT based patient dose calculations for static field and IMRT. In cases of VMAT plans, the difference between AAA and MC is <0.5%. The clinical impact resulting from the error on the calculated organ doses were analyzed by using dose-volume histograms. Although the AAA algorithm significantly underestimated the out-of-field doses, the clinical impact on the calculated organ doses in out-of-field regions may not be significant in practice due to very low out-of-field doses relative to the target dose.
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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) | 0.000 | 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) | 0.000 | 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".