Sci-Thur AM: Planning - 02: Validation of XiO's eMC module using Gafchromic EBT3 films and triple channel dosimetry
Bibliographic record
Abstract
The aim of this study is to validate the electron Monte Carlo module implemented in XiO, a treatment planning system commercialized by Elekta CMS inc. Two types of phantoms were investigated: homogeneous water phantoms with irregular surfaces and phantoms containing slab and 3D heterogeneities. The phantoms were CT scanned, and dose to water calculations were performed in the eMC module using 2 ×2 × 2 mm2 voxels and a mean relative statistical uncertainty of 0.5%. Concurrently, Gafchromic EBT3 film measurements were performed in the same phantoms. To obtain reliable absolute dose readings from the films, a new method using triple channel dosimetry in the Film QA Pro software was developed. The accuracy of the proposed method was determined empirically and an uncertainty of ±1.5% was found over the range [75, 800] cGy. Dose comparisons between film and simulations were done using an in-house MATLAB program. XiO's eMC module provides accurate dose distributions in the presence of surface irregularities and slab heterogeneities for 12 MeV beams. In the presence of 3D heterogeneities, the percent dose difference comparisons highlighted the need to perform 3D gamma comparisons. In conclusion, the electron Monte Carlo module offered in the XiO treatment planning system is promising and could greatly improve the accuracy of clinical dose calculations. The validation of the software is ongoing, notably concerning more complex phantom geometries. Small field calculations, oblique incidences and cutout factors will also be investigated.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".