First macro Monte Carlo based commercial dose calculation module for electron beam treatment planning—new issues for clinical consideration
Bibliographic record
Abstract
The purpose of this study is to present our experience of commissioning, testing and use of the first commercial macro Monte Carlo based dose calculation algorithm for electron beam treatment planning and to investigate new issues regarding dose reporting (dose-to-water versus dose-to-medium) as well as statistical uncertainties for the calculations arising when Monte Carlo based systems are used in patient dose calculations. All phantoms studied were obtained by CT scan. The calculated dose distributions and monitor units were validated against measurements with film and ionization chambers in phantoms containing two-dimensional (2D) and three-dimensional (3D) type low- and high-density inhomogeneities at different source-to-surface distances. Beam energies ranged from 6 to 18 MeV. New required experimental input data for commissioning are presented. The result of validation shows an excellent agreement between calculated and measured dose distributions. The calculated monitor units were within 2% of measured values except in the case of a 6 MeV beam and small cutout fields at extended SSDs (>110 cm). The investigation on the new issue of dose reporting demonstrates the differences up to 4% for lung and 12% for bone when 'dose-to-medium' is calculated and reported instead of 'dose-to-water' as done in a conventional system. The accuracy of the Monte Carlo calculation is shown to be clinically acceptable even for very complex 3D-type inhomogeneities. As Monte Carlo based treatment planning systems begin to enter clinical practice, new issues, such as dose reporting and statistical variations, may be clinically significant. Therefore it is imperative that a consistent approach to dose reporting is used.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".