Poster — Thur Eve — 12: Evaluation of an Electron Monte Carlo Dose Calculation Algorithm for Treatment Planning
Bibliographic record
Abstract
Purpose: To evaluate the accuracy of the electron Monte Carlo (eMC) dose calculation algorithm included in the Eclipse treatment planning system. Method and Materials: Multiple tests were planned to explore the system behaviour in configurations encountered in clinical practices. The first series of test were performed in a homogeneous water phantom. Measured and eMC calculated dose distributions were compared for different combinations of energy/applicator/depth. Three different inhomogeneous solid phantoms simulating high and low density materials were constructed to explore various heterogeneity conditions: a small air cylinder, a lung phantom and a chest wall phantom. In this work, the lung phantom, which consists of cork slabs, is described in details. In all cases, comparisons are made between measurements, carried out by means of XV‐2 films, and eMC and Pinnacle computations. Finally, the evaluation of the eMC algorithm for clinical case, a nose cancer, was conducted. Results: The eMC validation in the water phantom and in the lung phantom is shown to be accurate. The maximum observed discrepancy between measurements and eMC is only 2.5%. For the lung case, Pinnacle does not correctly model the electron scattering. The retrospective study of the clinical case, which presents scatter perturbations by air cavities, shows planar dose difference up to 20% between Pinnacle and eMC. Conclusion: eMC algorithm showed good agreements with measurements in simple homogeneous and heterogeneous phantoms. Comparatively to the electron pencil beam algorithms of Pinnacle, the eMC calculations more precisely predict 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.008 | 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".