Sci‐Thur PM – Brachytherapy 03: Identifying the impact of seroma visualization on permanent breast seed implant brachytherapy
Bibliographic record
Abstract
Purpose: Uncertainties in target identification can reduce treatment accuracy in permanent breast seed implant (PBSI) brachytherapy. This study evaluates the relationship between seroma visualization and seed placement accuracy. Methods: Spatially co‐registered CT and 3D ultrasound (US) images were acquired for 10 patients receiving PBSI. Seromas were retrospectively contoured independently by 3 radiation oncologists on both CT and US and respective consensus volumes were defined, CTVCT and CTVUS. The seroma clarity and inter‐user conformity index (CI), as well as inter‐modality CI, volume, and positional differences were evaluated. Correlations with seed placement accuracy were then assessed. CTVs were expanded by 1.25cm to create PTVCT and PTVUS and evaluate the conformity with PTVClinical (CTVClinical+1.25cm) used in treatment planning. The change in PTV coincidence by expanding PTVClinical by 0.25cm was determined. Results: CTVUS were a mean 68 ± 12% smaller than CTVCT and generally had improved clarity and inter‐user conformity. No correlations between seed displacement and CTVUS‐CTVCT positional difference or CI were observed. Greater seed displacements were associated with larger CTVUS‐CTVCT volume differences (r=−0.65) and inter‐user CT CI (r=−0.74). A median (range) 88% (71–99%) of PTVCT and 83% (69–100%) of PTVUS were contained within PTVClinical. Expanding treatment margins to 1.5cm increased coincidence to 98% (86–100%) and 94% (82–100%), respectively. Conclusions: Differences in seroma visualization impacts seed displacement in PBSI. Reducing dependence on CT by incorporating 3DUS into target identification, or expanding CT‐based treatment margins to 1.5cm may reduce or mitigate uncertainties related to seroma visualization.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".