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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".