Coupling I‐125 permanent implant prostate brachytherapy Monte Carlo dose calculations with radiobiological models
Bibliographic record
Abstract
PURPOSE: To investigate the coupling of radiobiological models with patient-specific Monte Carlo (MC) dose calculations for permanent implant prostate brachytherapy (PIPB). To compare radiobiological indices evaluated with different radiobiological models using MC and simulated AAPM TG-43 dose calculations. METHODS: Three-dimensional dose distributions previously computed using MC techniques with two types of patient models, TG43sim (AAPM TG-43 water-based conditions) and MCDmm (realistic tissues and interseed effects), for 613 PIPB patients are coupled with biological dose and tumour control probability (TCP) models. Two approaches and their extensions are considered to evaluate biological doses, biologically effective dose (BED) and isoeffective dose (IED), as well as two methods to evaluate TCP. Three novel extensions of equivalent uniform biologically effective dose (EUBED) are suggested which consider the spatial distribution of doses within the target volume. Adopted radiobiological model parameter values (α, β, etc) are those suggested by AAPM TG-137, and sensitivity to parameter choice is discussed. RESULTS: MCDmm dose calculations can reveal low doses in the prostate target volume, due to tissue heterogeneities or inter-seed effects; considering these low doses in EUBED calculations can lower TCP estimates by up to 70%, with largest differences in patients with calcifications. There are large variations in biological doses and TCPs evaluated over the 613 patient cohort for each radiobiological model considered, reflecting the spectrum of physical doses calculated for these patients with either MCDmm or TG43sim. Depending on the model details, BED, IED and EUBED are, on average, 6.0-9.8%, 7.4-9.2% and 1.8-15% higher, respectively, with TG43sim than MCDmm. TCP estimates computed using MCDmm dose distributions are much lower than expected based on past treatment outcome studies, suggesting a need to re-assess model parameters when evaluating radiobiological indices coupled with heterogeneous tissue model-based dose calculations. CONCLUSIONS: Cohort average differences in biological dose and TCP estimates between radiobiological models are generally larger than differences for any one radiobiological model evaluated with TG43sim or MCDmm dose calculations. However, heterogeneous tissue dose calculations, like MCDmm, can identify clinically-relevant low dose volumes, e.g., in patients with calcifications, which would otherwise be missed with TG-43. In addition to affecting physical dose distributions, these low dose volumes can largely impact radiobiological dose and TCP estimates, which further motivates the clinical implementation of model-based dose calculations for PIPB.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".