Evaluation of an electron Monte Carlo dose calculation algorithm for treatment planning
Bibliographic record
Abstract
The purpose of this study is to evaluate the accuracy of the electron Monte Carlo (eMC) dose calculation algorithm included in a commercial treatment planning system and compare its performance against an electron pencil beam algorithm. Several tests were performed to explore the system's behavior in simple geometries and in configurations encountered in clinical practice. The first series of tests were executed in a homogeneous water phantom, where experimental measurements and eMC-calculated dose distributions were compared for various combinations of energy and applicator. More specifically, we compared beam profiles and depth-dose curves at different source-to-surface distances (SSDs) and gantry angles, by using dose difference and distance to agreement. Also, we compared output factors, we studied the effects of algorithm input parameters, which are the random number generator seed, as well as the calculation grid size, and we performed a calculation time evaluation. Three different inhomogeneous solid phantoms were built, using high- and low-density materials inserts, to clinically simulate relevant heterogeneity conditions: a small air cylinder within a homogeneous phantom, a lung phantom, and a chest wall phantom. We also used an anthropomorphic phantom to perform comparison of eMC calculations to measurements. Finally, we proceeded with an evaluation of the eMC algorithm on a clinical case of nose cancer. In all mentioned cases, measurements, carried out by means of XV-2 films, radiographic films or EBT2 Gafchromic films. were used to compare eMC calculations with dose distributions obtained from an electron pencil beam algorithm. eMC calculations in the water phantom were accurate. Discrepancies for depth-dose curves and beam profiles were under 2.5% and 2 mm. Dose calculations with eMC for the small air cylinder and the lung phantom agreed within 2% and 4%, respectively. eMC calculations for the chest wall phantom and the anthropomorphic phantom also showed a positive agreement with the measurements. The retrospective dosimetric comparison of a clinical case, which presented scatter perturbations by air cavities, showed a difference in dose of up to 20% between pencil beam and eMC algorithms. When comparing to the pencil beam algorithm, eMC calculations are definitely more accurate at predicting large dose perturbations due to inhomogeneities.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".