Validation of an electron Monte Carlo dose calculation algorithm in the presence of heterogeneities using EGSnrc and radiochromic film measurements
Bibliographic record
Abstract
The purpose of this study is to validate Eclipse's electron Monte Carlo algorithm (eMC) in heterogeneous phantoms using radiochromic films and EGSnrc as a reference Monte Carlo algorithm. Four heterogeneous phantoms are used in this study. Radiochromic films are inserted in these phantoms, including in heterogeneous media, and the measured relative dose distributions are compared to eMC calculations. Phantoms A, B, and C contain 1D heterogeneities, built with layers of lung- (phantom A) and bone- (phantoms B and C) equivalent materials sandwiched in Plastic Water. Phantom D is a thorax anthropomorphic phantom with 2D lung heterogeneities. Electron beams of 6, 9, 12 and 18 MeV from a Varian Clinac 2100 are delivered to these phantoms with a 10 × 10 cm2 applicator. Monte Carlo simulations with an independent algorithm (EGSnrc) are also used as a reference tool for two purposes: (1) as a second validation of the eMC dose calculations, and (2) to calculate the stopping power ratio between radiochromic films and bone medium, when dose is measured inside the heterogeneity. Percent depth dose (PDD) film measurements and eMC calculations agree within 2% or 3 mm for phantom A, and within 3% or 3 mm for phantoms B and C for almost all beam energies. One exception is observed with phantom B and the 6 MeV, where measured PDDs and those calculated with eMC differ by up to 4 mm. Gamma analysis of the measured and calculated 2D dose distributions in phantom D agree with criteria of 3%, 3mm for 9, 12, and 18 MeV beams, and criteria of 5%, 3 mm for the 6 MeV beam. Dose calculations in heterogeneous media with eMC agree within 3% or 3 mm with radiochromic film measurements. Six (6) MeV beams are not modeled as accurately as other beam energies. The eMC algorithm is suitable for clinical dose calculations involving lung and bone.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".