Isolating the impact of tissue heterogeneities in high dose rate brachytherapy treatment of the breast
Bibliographic record
Abstract
Background and purpose Clinical brachytherapy treatment planning is performed assuming the patient is composed entirely of water and infinite in size. In this work, the effects of this assumption on calculated dose were investigated by comparing dose to water in water (D w,w ) in an unbound phantom mimicking TG-43 conditions, and dose to medium in medium (D m,m ) for breast cancer patients treated with high dose rate brachytherapy. Materials and methods Treatment plans for 123 breast cancer patients were recalculated with a Monte Carlo-based treatment planning software. The dwell times and dwell positions were imported from the clinical treatment planning system. The dose was computed and reported as D w,w and D m,m . Dose-volume histogram (DVH) metrics were evaluated for target volumes and organs at risk. Results D w,w overestimated the dose for most studied DVH metrics. The largest median overestimations between D m,m and D w,w were seen for the planning target volume (PTV) V 200% (5.8%), lung D 0.1 cm 3 (6.0%) and skin D 0.1 cm 3 (4.2%). The differences between D m,m and D w,w were statistically significant for all investigated DVH metrics . The PTV V 90% had the smallest deviation (0.7%). Conclusion There was a significant difference in the DVH metrics studied when tissue heterogeneities and patient-specific scattering are accounted for in high dose rate breast brachytherapy. However, for the studied patient cohort, the clinical coverage goal (PTV V 90% ), had the smallest deviation.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".