Clinical implementation of an open-source Monte Carlo system across multiple centers for permanent implant brachytherapy dose evaluation
Bibliographic record
Abstract
PURPOSE: This study aims to clinically implement eb_gui, a user-friendly toolkit for Monte Carlo simulations utilizing egs_brachy, in the context of low-dose-rate (LDR) brachytherapy for prostate and breast cancers. METHODS: I). Utilizing Digital Imaging and Communications in Medicine (DICOM) files, the open-source interface eb_gui was employed to compute doses. The commissioning process involved comparing eb_gui results against clinical TG-43 treatment planning system (TG43-TPS) calculations, encompassing point-by-point differences across 3D dose distributions, dose volume histograms, and dose metrics. Additionally, patient-specific dose distributions were computed using eb_gui's full-tissue models (TG186-MC) and compared against TG-43 Monte Carlo calculations (TG43-MC) across multiple cancer centers. RESULTS: Excellent agreement was observed between TG43-TPS and TG43-MC calculated doses, with point-to-point differences of less than 1 Gy (∼1% of prescription dose) for breast and prostate cases. Comparisons between multicenter TG186-MC and TG43-MC doses highlighted discrepancies that underscore the limitations of the TG-43 formalism and affirming the necessity for a model-based dose calculation algorithm (MBDCA). CONCLUSION: This study successfully developed a series of test cases and a commissioning workflow for implementing eb_gui in LDR brachytherapy across multiple centers. The findings underscore the potential of TG-186 MBDCA to enhance the precision of patient dosimetry and improve the accuracy of treatment outcome predictions in LDR brachytherapy. This work represents a significant step toward broader adoption of advanced dose calculation methodologies in clinical practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".