RapidBrachyTG43: A Geant4‐based TG‐43 parameter and dose calculation module for brachytherapy dosimetry
Bibliographic record
Abstract
BACKGROUND: -emitting brachytherapy sources. TG-43U1 and related reports provide consensus datasets of TG-43 parameters derived from various published measured data and Monte Carlo simulations. These data are used to perform standardized and fast dose calculations for brachytherapy treatment planning. PURPOSE: Monte Carlo TG-43 dosimetry parameters are commonly derived to characterize novel brachytherapy sources. RapidBrachyTG43 is a module of RapidBrachyMCTPS, a Monte Carlo-based treatment planning system, designed to automate this process, requiring minimal user input to prepare Geant4-based Monte Carlo simulations for a source. RapidBrachyTG43 may also perform a TG-43 dose to water-in-water calculation for a plan, substantially accelerating the same calculation performed using RapidBrachyMCTPS's Monte Carlo dose calculation engine. METHODS: Co, and were benchmarked to published data. TG-43 dose to water was calculated for a clinical breast brachytherapy plan and was compared to a Monte Carlo dose calculation with all patient tissues, air, and catheters set to water. RESULTS: TG-43 parameters for the three simulated sources agreed with benchmark datasets within tolerances specified by the High Energy Brachytherapy Dosimetry working group. A gamma index comparison between the TG-43 and Monte Carlo dose-to-water calculations with a dose difference and difference to agreement criterion of 1%/1 mm yielded a 98.9% pass rate, with all relevant dose volume histogram metrics for the plan agreeing within 1%. Performing a TG-43-based dose calculation provided an acceleration of dose-to-water calculation by a factor of 165. CONCLUSIONS: Determination of TG-43 parameter data for novel brachytherapy sources may now be facilitated by RapidBrachyMCTPS. These parameter datasets and existing consensus or published datasets may also be used to determine the TG-43 dose for a plan in RapidBrachyMCTPS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".