Towards an objective evaluation of tolerances for beam modeling in a treatment planning system
Bibliographic record
Abstract
The performance of a convolution/superposition based treatment planning system depends on the ability of the dose calculation algorithm to accurately account for physical interactions taking place in the tissue, key components of the linac head and on the accuracy of the photon beam model. Generally the user has little or no control over the performance of the dose calculation algorithm but is responsible for the accuracy of the beam model within the constraints imposed by the system. This study explores the dosimetric impact of limitations in photon beam modeling accuracy on complex 3D clinical treatment plans. A total of 70 photon beam models was created in the Pinnacle treatment planning system. Two of the models served as references for 6 MV and 15 MV beams, while the rest were created by perturbing the reference models in order to produce specific deviations in specific regions of the calculated dose profiles (central axis and transverse). The beam models were then used to generate 3D plans on seven CT data sets each for four different treatment sites (breast and conformal prostate, lung and brain). The equivalent uniform doses (EUD) of the targets and the principal organs at risk (OARs) of all plans ( approximately 1000) were calculated and compared to the EUDs delivered by the reference beam models. In general, accurate dosimetry of the target is most greatly compromised by poor modeling of the central axis depth dose and the horns, while the EUDs of the OARs exhibited the greatest sensitivity to beam width accuracy. Based on the results of this analysis we suggest a set of tolerances to be met during commissioning of the beam models in a treatment planning system that are consistent in terms of clinical outcomes as predicted by the EUD.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".