SU‐FF‐T‐406: Toward a More Accurate Dose Calculation Technique Using a Semiautomatic Organ Contouring in Monte Carlo Post‐Implant Assessment of Breast LDR Brachytherapy
Bibliographic record
Abstract
Purpose: We assess the impact of the uncertainty of glandular/adipose tissue proportion on dose distribution inside the breast for LDR brachytherapy. Furthermore, we propose a semiautomatic tissue segmentation method to be used in Monte Carlo (MC) calculations for breast LDR patients. Methods & materials: Post‐implant CT exams of five breast brachytherapy patients are imported into our Geant4 MC platform. The CTV and the PTV were contoured by the oncologist; the external contours of the breast, skin, lungs and the ribs were made manually by a physicist. In a first step, the breast is assigned with different proportions of glandular/adipose tissue (ρ = 100/0, 75/25, 50/50, 25/75, 0/100) in order to study the impact of this variation on clinical dosimetries. In a second step, a density‐based semiautomatic segmentation of breast tissue is used to identify glandular from adipose regions inside the breast and to create a more representative breast anatomy with appropriate chemical compositions. A TG43‐like simulation (SMC: Superposition MC) is also performed for each patient. Does‐Volume‐Histograms are used to visualize the effect of dose reduction. Results: In all patients, the glandular/adipose proportion, affects the dose distribution across the breast. The higher the adipose proportion in the breast tissue, the larger is the dose reduction across the organ. The D90 clinical parameter is reduced by up to 22% when the breast is entirely made of adipose. The semiautomatic contouring enabled a patient‐dependent segmentation of the glandular and adipose regions leading to more accurate dose calculations for each patient. Conclusion: Using realistic chemical compositions in MC simulations is achievable for MC calculations. Unlike external‐beam radiotherapy, the low‐energy emission of 103Pd is strongly affected by the heterogeneities adipose proportion in breast. A patient‐dependant glandular/adipose segmentation in breast is important for accurate dose determinations in MC, especially for breast LDR 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".