Changes in dose with segmentation of breast tissues in Monte Carlo calculations for low‐energy brachytherapy
Bibliographic record
Abstract
PURPOSE: To investigate the use of various breast tissue segmentation models in Monte Carlo dose calculations for low-energy brachytherapy. METHODS: The EGSnrc user-code BrachyDose is used to perform Monte Carlo simulations of a breast brachytherapy treatment using TheraSeed Pd-103 seeds with various breast tissue segmentation models. Models used include a phantom where voxels are randomly assigned to be gland or adipose (randomly segmented), a phantom where a single tissue of averaged gland and adipose is present (averaged tissue), and a realistically segmented phantom created from previously published numerical phantoms. Radiation transport in averaged tissue while scoring in gland along with other combinations is investigated. The inclusion of calcifications in the breast is also studied in averaged tissue and randomly segmented phantoms. RESULTS: In randomly segmented and averaged tissue phantoms, the photon energy fluence is approximately the same; however, differences occur in the dose volume histograms (DVHs) as a result of scoring in the different tissues (gland and adipose versus averaged tissue), whose mass energy absorption coefficients differ by 30%. A realistically segmented phantom is shown to significantly change the photon energy fluence compared to that in averaged tissue or randomly segmented phantoms. Despite this, resulting DVHs for the entire treatment volume agree reasonably because fluence differences are compensated by dose scoring differences. DVHs for the dose to only the gland voxels in a realistically segmented phantom do not agree with those for dose to gland in an averaged tissue phantom. Calcifications affect photon energy fluence to such a degree that the differences in fluence are not compensated for (as they are in the no calcification case) by dose scoring in averaged tissue phantoms. CONCLUSIONS: For low-energy brachytherapy, if photon transport and dose scoring both occur in an averaged tissue, the resulting DVH for the entire treatment volume is reasonably accurate because inaccuracies in photon energy fluence are compensated for by inaccuracies in localized dose scoring. If dose to fibroglandular tissue in the breast is of interest, then the inaccurate photon energy fluence calculated in an averaged tissue phantom will result in inaccurate DVHs and average doses for those tissues. Including calcifications necessitates the use of proper tissue segmentation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".