SU‐E‐T‐669: Clinical Implementation of a Commercial Monte Carlo Treatment Planning System for Electron Beams
Bibliographic record
Abstract
Purpose: Several Monte Carlo (MC) based treatment planning systems are commercially available for electron beams and more users are implementing them in the clinical setting. In the case of MC based treatment planning systems in addition to the dose calculation accuracy one also needs to define the additional calculation parameters, such as the calculation voxel size, the choice between the computation of dose‐to‐medium or dose‐to‐water, and the number of histories and/or acceptable level of statistical uncertainty for the voxelized dose distribution. Methods: We report on the influence of these parameters on both calculation time and on the accuracy of calculated dose distributions for the XiO electron Monte Carlo (XiO eMC) software, a new treatment planning system for electron beams developed and commercialized by CMS incorporated. A comparison of radiochromic film measurements with simulated data using different parameters was performed for a complex heterogeneous phantom. Results: Dose calculations in a heterogeneous phantom are in very good agreement with film measurements, provided that appropriate simulation parameters are used (1% average statistical uncertainty, voxel size=2×2×2 mm cubed and dose‐ to‐water is computed). The rapid three‐dimensional dose gradients exhibited for these data highlight the need for a fully 3D dose comparison tool (currently under development) for electron beams treating complex heterogeneous geometries. Using a clinical XiO Linux workstation (8 processors each 3 GHz, 8.29 GB RAM), high resolution (2.5×2.5×2.5 mm cubed) low noise (<1% MRSU) simulations can be obtained in less than 4 and 8 minutes respectively for 9 and 17 MeV beams with a 10×10 cm squared applicator. Conclusions: XiO eMC calculated dose distributions agree very well with the experimental ones in both water tank and heterogeneous phantoms. More investigation may be required, however, to determine the optimal trade off between dosimetric accuracy and clinically acceptable computation times. Financial support has been provided from Elekta CMS incorporated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".