RapidBrachyMCTPS: a Monte Carlo-based treatment planning system for brachytherapy applications
Bibliographic record
Abstract
Abstract Despite being considered the gold standard for brachytherapy dosimetry, Monte Carlo (MC) has yet to be implemented into a software for brachytherapy treatment planning. The purpose of this work is to present RapidBrachyMCTPS, a novel treatment planning system (TPS) for brachytherapy applications equipped with a graphical user interface (GUI), optimization tools and a Geant4-based MC dose calculation engine, RapidBrachyMC. Brachytherapy sources and applicators were implemented in RapidBrachyMC and made available to the user via a source and applicator library in the GUI. To benchmark RapidBrachyMC, TG-43 parameters were calculated for the microSelectron v2 ( 192 Ir) and SelectSeed ( 125 I) source models and were compared against previously validated MC brachytherapy codes. The performance of RapidBrachyMC was evaluated for a prostate high dose rate case. To assess the accuracy of RapidBrachyMC in a heterogeneous setup, dose distributions with a cylindrical shielded/unshielded applicator were validated against film measurements in a Solid Water TM phantom. TG-43 parameters calculated using RapidBrachyMC generally agreed within 1%–2% of the results obtained in previously published work. For the prostate case, clinical dosimetric indices showed general agreement with Oncentra TPS within 1%. Simulation times were on the order of minutes on a single core to achieve uncertainties below 2% in voxels within the prostate. The calculation time was decreased further using the multithreading features of Geant4. In the comparison between MC-calculated and film-measured dose distributions, at least 95% of points passed the 3%/3 mm gamma index criteria in all but one case. RapidBrachyMCTPS can be used as a post-implant dosimetry toolkit, as well as for MC-based brachytherapy treatment planning. This software is especially well suited for the development of new source and applicator models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".