A Monte Carlo approach to the validation of a pencil beam algorithm used in treatment planning for conformal beam radiosurgery with static fields /
Bibliographic record
Abstract
Stereotactic radiosurgery with several static conformal beams shaped by a micro multileaf collimator (muMLC) is used for treating small irregularly shaped brain lesions. Specific requirements for this technique are a precise localization and positioning of the target (1mm) and a precise (1mm) and numerically accurate (+/-5%) dose delivery. In this work, a pencil beam algorithm based treatment planning software BrainScan 5.2 (Brainlab, Germany) is validated against measurements (diode, radiographic films) and Monte Carlo simulations (BEAMnrc and XVMC codes). The latter is required because of difficulties in obtaining precise and accurate dose measurements for small fields. A dedicated muMLC component module for the BEAMnrc code was developed as part of this project. Results show that Monte Carlo calculations agree with measured dose distributions to within 2% and/or 1 mm except for field sizes smaller than 1.2 cm where agreement is within 5% due to uncertainties in measured output factors. Comparison with the pencil beam algorithm calculations were performed for square and irregularly shaped fields at different incidence angles on rectangular and humanoid homogeneous phantoms. Results show that the pencil beam algorithm is suitable for radiosurgery although some differences were found in the comparison of interleaf leakage and beam profile penumbras.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".