Monte Carlo evaluation of a treatment planning system for helical tomotherapy in an anthropomorphic heterogeneous phantom and for clinical treatment plans
Bibliographic record
Abstract
Helical tomotherapy is an increasingly common form of intensity modulated radiation therapy that allows for image guided adaptive radiotherapy. Its treatment planning system (TPS) uses a convolution superposition algorithm for dose distribution calculations. The accuracy of this algorithm in the presence of heterogeneities was evaluated against Monte Carlo (MC) calculations and measurements. This work performed BEAMnrc-and DOSXYZnrc-based MC dose calculations of tomotherapy deliveries to a CIRS anthropomorphic heterogeneous phantom with typical clinical inverse planning and delivery settings. Point measurements with A1SL ion chambers and relative measurements with Kodak EDR2 film were carried out in the phantom. The experimental results were used to evaluate both the TPS and MC dose calculations. Furthermore, the dose distribution for a clinical head-and-neck cancer plan was calculated on the TPS and MC systems. The results support this MC system as a viable option for the accurate simulation of the tomotherapy process in the presence of heterogeneities where direct measurement may not be practical. Ion chamber measurements in the CIRS phantom suggested the TPS has an average relative difference of 2.3%, with the largest difference being -4.1% in one of the organs at risk. The MC system accurately predicted the dose to these measurement points within statistical uncertainty. The film measurements in the CIRS phantom demonstrated 90.7% (of pixels) agreed with the MC system using a +/-3%/3 mm acceptance criteria, where only 50.3% agreed with the TPS. In the clinical head-and-neck cancer plan evaluation where MC served as a reference against which to compare the TPS result, an average of 92.7% of the voxels within volumes of interest passed a 3%/3 mm criteria. The PTV54 showed the worst agreement with 85.4% of the volume passing the 3% /3 mm criteria. In general, the +/-3%/3 mm criterion was found to be a challenge for the TPS in the presence of lung inhomogeneity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".