Patient-specific Monte Carlo dose calculations for<sup>103</sup>Pd breast brachytherapy
Bibliographic record
Abstract
This work retrospectively investigates patient-specific Monte Carlo (MC) dose calculations for (103)Pd permanent implant breast brachytherapy, exploring various necessary assumptions for deriving virtual patient models: post-implant CT image metallic artifact reduction (MAR), tissue assignment schemes (TAS), and elemental tissue compositions. Three MAR methods (thresholding, 3D median filter, virtual sinogram) are applied to CT images; resulting images are compared to each other and to uncorrected images. Virtual patient models are then derived by application of different TAS ranging from TG-186 basic recommendations (mixed adipose and gland tissue at uniform literature-derived density) to detailed schemes (segmented adipose and gland with CT-derived densities). For detailed schemes, alternate mass density segmentation thresholds between adipose and gland are considered. Several literature-derived elemental compositions for adipose, gland and skin are compared. MC models derived from uncorrected CT images can yield large errors in dose calculations especially when used with detailed TAS. Differences in MAR method result in large differences in local doses when variations in CT number cause differences in tissue assignment. Between different MAR models (same TAS), PTV [Formula: see text] and skin [Formula: see text] each vary by up to 6%. Basic TAS (mixed adipose/gland tissue) generally yield higher dose metrics than detailed segmented schemes: PTV [Formula: see text] and skin [Formula: see text] are higher by up to 13% and 9% respectively. Employing alternate adipose, gland and skin elemental compositions can cause variations in PTV [Formula: see text] of up to 11% and skin [Formula: see text] of up to 30%. Overall, AAPM TG-43 overestimates dose to the PTV ([Formula: see text] on average 10% and up to 27%) and underestimates dose to the skin ([Formula: see text] on average 29% and up to 48%) compared to the various MC models derived using the post-MAR CT images studied herein. The considerable differences between TG-43 and MC models underline the importance of patient-specific MC dose calculations for permanent implant breast brachytherapy. Further, the sensitivity of these MC dose calculations due to necessary assumptions illustrates the importance of developing a consensus modelling approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".