A study to determine the optimal input parameters for the Monte Carlo simulation of a clinical linear accelerator
Bibliographic record
Abstract
Currently, commercial treatment planning systems are validated against Monte Carlo (MC) simulation in medical physics research. MC is the current gold standard to model the transport of radiation. In this study, a Monte Carlo package, Electron Gamma Shower from the National Research Council Canada (EGSnrc), is chosen to calculate the dose distribution for photon beams under standard reference and small field conditions and validated against measured data. \n \nIn a MC simulation of photon beam, 2 key components are needed; First, a photon beam source and second, a target medium. External beam radiotherapy is the most common form of radiotherapy for treating cancer, and a linear accelerator (LINAC) is used to deliver the radiation. A target medium can be of any material of interest for study or a human body for clinical application. \n \nEGSnrc is able to model a LINAC through its subroutine BEAMnrc. BEAMnrc models the geometry and materials of a commercial LINAC. However, the exact modeling of a commercial linac in BEAMnrc may not yield the best or optimal clinical beam distribution against actual measured data. As such, a few key LINAC parameters in BEAMnrc will have to be varied and simulated in a water phantom to produce a depth dose and lateral dose profile to match clinically measured results. Various parameters will be adjusted in the BEAMnrc LINAC model to derive a set of optimal parameters that produces the closest match between simulation and measured. They are the electron energy, the full width half maximum or FWHM of the electron beam and the jaw thickness. \n \nThe results of the study has shown that optimal parameters differs between different field sizes for the LINAC, contrary to recommendations by previous studies.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".