Improved heterogeneity handling in the collapsed cone dose engine for brachytherapy
Bibliographic record
Abstract
Abstract Background Model‐based dose calculation algorithms (MBDCA), such as the Advanced Collapsed cone Engine (ACE) in Oncentra Brachy® can be used to overcome the limitations of the TG‐43 formalism. ACE is a point kernel superposition algorithm that calculates the total dose separated into primary, first‐scatter, and multiple‐scatter dose. Albeit ACE yields accurate results under most circumstances, several studies have reported underestimations of the dose to cortical bone. These underestimations are likely caused by approximations in the handling of multiple‐scatter dose for non‐water media. Such would result in noticeable deviations where the multiple‐scatter is a considerable fraction of the total dose, that is, at greater distances from the source. Purpose To improve and test the accuracy of the multiple‐scatter dose component in the ACE algorithm to remedy its inaccuracy for non‐water geometries. Methods A careful analysis of the transport and absorption of the multiple‐scatter energy fluence revealed an inconsistency in the scaling of energy absorption ratios for non‐water media of the multiple‐scatter kernel. We implemented an updated algorithm version, ACEcorr, and tested it for three different geometries. All had a single 192Ir‐source at the center of a cubic water phantom with a box‐shaped heterogeneity of either cortical bone or air, positioned at different distances from the source. Dose distributions for the three cases were calculated with ACE and ACEcorr and compared to Monte Carlo simulations, using the percentage dose difference ratio as figure‐of‐merit. All dose calculation methods scored separately the dose deposited by primary, first‐scattered, and multiple‐scattered photons. Results The accuracy of the updated algorithm ACEcorr was superior to ACE. In the cortical bone heterogeneity, the mean percentage dose difference ratio for the total dose improved from to (in the worst case) by our update. Less impact was seen in the air heterogeneity, where both ACE and ACEcorr deviated less than 2% from the Monte Carlo results. The algorithm update mainly concerns the multiple‐scattered dose component, but an accompanying data processing update also had a small effect (0.5% difference) on the primary and first‐scattered dose. The calculation times were not affected. Conclusions The updates to ACE improved the accuracy of multiple‐scatter dose calculation for non‐water media, without increasing calculation times.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".