A graphical user interface for Monte Carlo dose calculations for brachytherapy with egs_brachy
Bibliographic record
Abstract
BACKGROUND: Recent advancements in brachytherapy necessitate precise dose calculations, transitioning from the traditional TG43 planning methods to the more sophisticated TG186 recommendations. However, the availability of accessible and efficient Monte Carlo (MC) codes capable of interfacing with clinical data for these advanced calculations remains limited. PURPOSE: This study presents and validates eb_gui, a graphical user interface designed to seamlessly integrate DICOM clinical data with egs_brachy, a fast MC dose calculation algorithm tailored for brachytherapy applications. METHODS: The egs_brachy graphical user interface (eb_gui) was developed using C++ with the Qt5 framework. Five benchmarking scenarios were employed to validate the use of eb_gui. Simulations were compared against three non-clinical test cases developed by the joint Working Group on Model-Based Dose Calculations (WG-MBDC), assessing local and global dose difference ratios with reference MC data. An interstitial HDR breast case and a low dose rate (LDR) prostate case were also analyzed, evaluating dose difference histograms, dose ratio maps, dose-volume histograms, and extracted dose metrics. RESULTS: For the three WG-MBDCA test cases, over 95% of evaluated voxels showed local dose differences of less than ± 0.40%, with all voxels demonstrating global dose differences within ± 0.02%. In the interstitial HDR breast case, over 95% of voxels exhibited global difference ratios within [-0.31%, +0.39%] relative to the reference dataset, while local difference ratios varied due to simulation conditions. Results of the LDR prostate test case simulations underscored the eb_gui's comprehensive capabilities and accuracy. CONCLUSION: eb_gui successfully bridges clinical data with egs_brachy MC simulations, facilitating advanced, patient-specific dose evaluations in accordance with TG186 recommendations. By releasing eb_gui as open-source software on GitHub, this study promotes widespread adoption within the clinical community, supporting the move toward more accurate and personalized brachytherapy treatments. This tool represents a significant advancement, enabling clinicians to conduct high-quality MC dose calculations efficiently.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.021 |
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".