Assessment of Eclipse electron Monte Carlo output prediction for various topologies
Bibliographic record
Abstract
Monte Carlo simulation is deemed to be the leading algorithm for accurate dose calculation with electron beams. Patient anatomy (contours and tissue densities) as well as irradiation geometry is accounted for. The accuracy of the Monitor Unit (MU) determination is one essential aspect of a treatment planning system. Patient-specific quality assurance of a Monte Carlo plan usually involves verification of the MUs with an independent simpler calculation approach, in which flat geometry is to be assumed. The magnitude of the discrepancies between flat and varied surfaces for a few scenarios has been investigated in this study. The ability to predict MUs for various surface topologies by the commercial electron Monte Carlo implementation from Varian Eclipse system (Eclipse eMC) has been evaluated and compared to the Generalized Gaussian Pencil Beam (GGPB) algorithm. Ten phantoms with different topologies were constructed of water-equivalent material. Measurements with a parallel plate ionization chamber were performed using these phantoms to gauge their relative impact on outputs for 6, 9, 12, 16, and 20MeV electron beams from a Varian TrueBeam with cone sizes ranging from 6 × 6 cm2 to 25 × 25 cm2. The corresponding Monte Carlo simulations of the measured geometries were carried out using the CT scans of these phantoms. The results indicated that the Eclipse eMC algorithm can predict these output changes within 3% for most scenarios. However, at the lowest energy, the discrepancy was the greatest, up to 6%. In comparison, the Eclipse GGPB algorithm had much worse agreement, with discrepancies up to 17% at the lowest energies.
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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 | 0.012 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".