Advancements in Monte Carlo Dose Calculations for Prostate and Breast Permanent Implant Brachytherapy
Bibliographic record
Abstract
Monte Carlo (MC) simulations of radiation transport may provide more accurate estimates of dose delivered to permanent implant brachytherapy patients compared to the clinical AAPM TG-43 dose calculation paradigm. However, MC dose calculations are burdened by sensitivity to required modelling assumptions, especially with low energy photon sources typical of permanent implant brachytherapy (20-30 keV). MC simulations require a detailed virtual model of the patient, often derived from post-treatment CT images containing imaging artifacts due to the brachytherapy sources present during image acquisition. For the first time, several metallic artifact reduction algorithms, of varied approach, are explored in phantom and clinical prostate brachytherapy CT images to determine their ability to mitigate artifacts and to quantify their sensitivity on dose calculations. Permanent implant breast brachytherapy is a challenge to model due to the radiologically different adipose and fibrogland soft tissues in and near the treatment volume. Further, the geometry of breast treatments is especially non-water like suggesting the clinical TG-43 dose calculation paradigm may yield significantly inaccurate results. The dose calculation sensitivity due to metallic artifact reduction, tissue differentiation approach and simulated tissue composition are explored in permanent implant breast brachytherapy clinical patient data, in addition to presenting differences between realistic and TG-43 conditions for target and organ-at-risk dosimetric endpoints. This work presents the largest cohort of permanent implant prostate brachytherapy MC dose calculations published to-date, providing quantitative differences between realistic and TG-43 conditions and a detailed sensitivity analysis of organ-at-risk simulated tissue composition, calcification modelling approach and calcification tissue composition. Clinical radiotherapy is increasingly considering radiobiological endpoints to judge treatment quality. Various radiobiological indices are evaluated using a large cohort of prostate brachytherapy dose calculations to explore the differences between these models and differences between applying TG-43 or realistic tissue MC dose calculations. This work identifies challenges related to applying dose calculations in heterogeneous tissue models to existing radiobiological models. The contributions of this thesis improve the understanding of realistic brachytherapy dose calculations, pave the way for the clinical adoption of Monte Carlo dose calculations and challenge and build upon the current frontier of radiobiological modelling for permanent implant brachytherapy.
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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 | 0.001 | 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 | 0.000 | 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) | 0.004 | 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".