Evaluation of the first commercial Monte Carlo dose calculation engine for electron beam treatment planning
Bibliographic record
Abstract
The purpose of this study is to perform a clinical evaluation of the first commercial (MDS Nordion, now Nucletron) treatment planning system for electron beams incorporating Monte Carlo dose calculation module. This software implements Kawrakow's VMC++ voxel-based Monte Carlo calculation algorithm. The accuracy of the dose distribution calculations is evaluated by direct comparisons with extensive sets of measured data in homogeneous and heterogeneous phantoms at different source-to-surface distances (SSDs) and gantry angles. We also verify the accuracy of the Monte Carlo module for monitor unit calculations in comparison with independent hand calculations for homogeneous water phantom at two different SSDs. All electron beams in the range 6-20 MeV are from a Siemens KD-2 linear accelerator. We used 10,000 or 50,000 histories/cm2 in our Monte Carlo calculations, which led to about 2.5% and 1% relative standard error of the mean of the calculated dose. The dose calculation time depends on the number of histories, the number of voxels used to map the patient anatomy, the field size, and the beam energy. The typical run time of the Monte Carlo calculations (10,000 histories/cm2) is 1.02 min on a 2.2 GHz Pentium 4 Xeon computer for a 9 MeV beam, 10 x 10 cm2 field size, incident on the phantom 15 x 15 x 10 cm3 consisting of 31 CT slices and voxels size of 3 x 3 x 3 mm3 (total of 486,720 voxels). We find good agreement (discrepancies smaller than 5%) for most of the tested dose distributions. We also find excellent agreement (discrepancies of 2.5% or less) for the monitor unit calculations relative to the independent manual calculations. The accuracy of monitor unit calculations does not depend on the SSD used, which allows the use of one virtual machine for each beam energy for all arbitrary SSDs. In some cases the test results are found to be sensitive to the voxel size applied such that bigger systematic errors (>5%) occur when large voxel sizes interfere with the extensions of heterogeneities or dose gradients because of differences between the experimental and calculated geometries. Therefore, user control over voxelization is important for high accuracy electron dose calculations.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".