Sci-Sat AM(1): Planning - 10: Evaluation of a New Commercial Monte-Carlo Treatment Planning System for Electrons
Bibliographic record
Abstract
It has long been understood that the Monte Carlo (MC) method is the most effective means for accurately computing the dose delivered by clinical electron beams. Every commercial implementation of the MC method involves design compromises and the possibility of error. It is important, therefore, that each implementation is independently validated under conditions similar to those found in the clinic. In this abstract, we present the initial stages of validation for the XiO electron Monte Carlo (XiO eMC) software, a new treatment planning system for electron beams developed and commercialized by CMS incorporated. In this abstract we present a limited set of comparisons of calculated and experimental data for homogeneous water phantoms and for a 3D heterogeneous phantom meant to approximate the geometry of a trachea and spine. All Monte Carlo calculated and measured output factors agree within the estimated standard error for standard and extended SSD for open applicators and cerrobend cutouts with the exception of the smallest cutout size (2×2cm2) for 17 MeV at extended SSD. We also found good agreement between calculated and experimental depth dose curves and dose profiles. Dose calculations in heterogeneous phantoms are also in a very good agreement with measurements, given an estimated positional uncertainty of ±0.1cm in the depth direction, and provided that appropriate calculation voxel sizes are used for a given geometry. Acknowledgment: The authors would like to acknowledge the excellent technical support provided by Dr. J C Satterthwaite of Elekta CMS Software.
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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 | 0.005 |
| 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) | 0.017 | 0.002 |
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".