A comparison of Monte Carlo, anisotropic analytical algorithm (AAA) and Acuros XB algorithms in assessing dosimetric perturbations during enhanced dynamic wedged radiotherapy deliveries in heterogeneous media
Bibliographic record
Abstract
Abstract Background A comparison of anisotropic analytical algorithm (AAA) and Acuros XB (AXB) dose calculation algorithms with Electron Gamma Shower (EGSnrc) Monte Carlo (MC) for modelling lung and bone heterogeneities encountered during enhanced dynamic wedged (EDWs) radiotherapy dose deliveries was carried out. Materials and methods In three heterogenous slab phantoms: water–bone, lung–bone and bone–lung, wedged percentage depth doses with EGSnrc, AAA and AXB algorithms for 6 MV photons for various field sizes (5×5, 10×10 and 20×20 cm2) and EDW angles (15°, 30°, 45° and 60°) have been scored. Results For all the scenarios, AAA and AXB results were within ±1% of the MC in the pre-inhomogeneity region. For water–bone AAA and AXB deviated by 6 and 1%, respectively. For lung–bone an underestimation in lung (AAA: 5%, AXB: 2%) and overestimation in bone was observed (AAA: 13%, AXB: 4%). For bone–lung phantom overestimation in bone (AAA: 7%, AXB: 1%), a lung underdosage (AAA: 8%, AXB: 5%) was found. Post bone up to 12% difference in the AAA and MC results was observed as opposed to 6% in case of AXB. Conclusion This study demonstrated the limitation of the AAA (in certain scenarios) and accuracy of AXB for dose estimation inside and around lung and bone inhomogeneities. The dose perturbation effects were found to be slightly dependent on the field size with no obvious EDW dependence.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".