Monte Carlo Dose Calculations for Permanent Breast Seed Implant Brachytherapy
Bibliographic record
Abstract
In order to investigate the use of patient-specific (rather than water-based) models for permanent breast seed implant (PBSI) brachytherapy, a retrospective study of 35 PBSI patients is performed.Virtual detailed-tissue patient models are created and overlaid with Pd-103 seed geometries, allowing for simulations with egs brachy, a new Monte Carlo code.Considerable discrepancies in dose distributions are demonstrated.Target dose metrics are 4-26% higher using TG43 assumptions, skin metrics are underestimated by up to 66.5%, and large disparities are observed in heart, lung, and rib doses.The sensitivity of dose distributions to assumptions in model creation is examined.Individualized adipose-gland segmentation thresholds and realistic seed orientations are shown to be important for accurate modeling.Radioprotective lead shielding has a negligible impact on skin dose.This thesis demonstrates the importance of detailed patient modeling for PBSI brachytherapy, illustrating the shortcomings of TG43-based simulations and contributing to the future clinical implementation of model-based dose calculation algorithms.I'd like to thank all the people who helped this thesis take shape over the last two years.First, I'd like to thank my family.Without the support of Patsy, Lorne, Connor, and Joe, I would have never been able to successfully finish this project.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".