SU-FF-T-252: HDR Endorectal Brachytherapy: Quality Assurance
Bibliographic record
Abstract
Purpose: Fractionated high‐dose rate endorectal brachytherapy has been developed in our institution as a pre‐operative down‐staging modality. Since the treatment is fractionated (26 Gy/4) it is essential to reproduce the treatment planningdose distribution on a daily basis. In this paper we present the Quality Assurance (QA) steps developed to ensure patient based daily dose reproducibility. Method and Materials: The applicator used has a cylindrical symmetry. In addition to the applicator auto‐radiographs used for the catheter physical length determination, there are two additional steps that have to be performed in order to reproduce a treatment planningdose distribution on a daily basis. Another correction has to be performed on a daily basis for rotation of the catheter channels. For this purpose, we use uniquely coded “dummy” source inserts that show themselves on a daily radiograph. The applicator is rotated and radiograph repeated until perfect alignment is achieved. Results: Since the applicator might not be placed to the same depth inside the rectal lumen, there is a shift along the catheter axis that has to be performed on a daily basis. The amount of shift is determined by comparison of a daily radiograph and treatment planningDRR.Conclusions: Reproduction of the treatment planningdose distribution on a daily basis is crucial for the success of the fractionated 3D based brachytherapy treatments. Due to the cylindrical symmetry, two types of adjustments are necessary: applicator rotation and dose distribution shift along the applicator axis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".