The accuracy of Acuros XB algorithm for radiation beams traversing a metallic hip implant — comparison with measurements and Monte Carlo calculations
Bibliographic record
Abstract
In this study, the clinical benefit of the improved accuracy of the Acuros XB (AXB) algorithm, implemented in a commercial radiotherapy treatment planning system (TPS), Varian Eclipse, was demonstrated with beams traversing a high-Z material. This is also the first study assessing the accuracy of the AXB algorithm applying volumetric modulated arc therapy (VMAT) technique compared to full Monte Carlo (MC) simulations. In the first phase the AXB algorithm was benchmarked against point dosimetry, film dosimetry, and full MC calculation in a water-filled anthropometric phantom with a unilateral hip implant. Also the validity of the full MC calculation used as reference method was demonstrated. The dose calculations were performed both in original computed tomography (CT) dataset, which included artifacts, and in corrected CT dataset, where constant Hounsfield unit (HU) value assignment for all the materials was made. In the second phase, a clinical treatment plan was prepared for a prostate cancer patient with a unilateral hip implant. The plan applied a hybrid VMAT technique that included partial arcs that avoided passing through the implant and static beams traversing the implant. Ultimately, the AXB-calculated dose distribution was compared to the recalculation by the full MC simulation to assess the accuracy of the AXB algorithm in clinical setting. A recalculation with the anisotropic analytical algorithm (AAA) was also performed to quantify the benefit of the improved dose calculation accuracy of type 'c' algorithm (AXB) over type 'b' algorithm (AAA). The agreement between the AXB algorithm and the full MC model was very good inside and in the vicinity of the implant and elsewhere, which verifies the accuracy of the AXB algorithm for patient plans with beams traversing through high-Z material, whereas the AAA produced larger discrepancies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".