The accuracy of EGSnrc, Geant4 and PENELOPE Monte Carlo systems for the simulation of electron scatter in external beam radiotherapy
Bibliographic record
Abstract
Three widely used Monte Carlo systems were benchmarked against recently published measurements of the angular distribution of 13 MeV and 20 MeV electrons scattered from foils of different atomic numbers and thicknesses. Source and geometry were simulated in detail to calculate electron fluence profiles 118.2 cm from the exit window. Results were compared to the measured fluence profiles and the characteristic angle where the fluence drops to 1/e of its maximum value. EGSnrc and PENELOPE results, on average, agreed with measurement within 1 standard deviation experimental uncertainty, with EGSnrc estimating slightly lower scatter than measurement and PENELOPE slightly higher scatter. Geant4.9.2 overestimated the characteristic angle for the lower atomic number foils by as much as 10%. Retuning of the scatter distributions in Geant4 led to a much better agreement with measurement, close to that achieved with the other codes. The 3% differences from measurement seen with all codes for at least some of the foils would result in clinically significant errors in the fluence profiles (2%/4 mm), given accurate knowledge of the electron source and treatment head geometry used in radiotherapy. Further improvement in simulation accuracy is needed to achieve 1%/1 mm agreement with measurement for the full range of beam energies, foil atomic number and thickness used in radiotherapy. EGSnrc would achieve this accuracy with an increase in thickness of the mylar sheets in the monitor chamber, PENELOPE with a decrease in thickness.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".