WE‐A‐108‐11: Patient Specific Quality Assurance Tool in Rectal Brachytherapy
Bibliographic record
Abstract
Purpose: To design and evaluate a patient‐specific quality assurance program for HDR Brachytherapy of rectal cancer. Methods: A solid water phantom with a rectal applicator positioned in the center was designed. Dose distributions were measured with two sheets of EBT3 model GAFCHROMIC™ film placed at 2 cm above and 5 cm below the center of the applicator. Treatment plans of 12 patients were studied, with each plan recalculated for the phantom geometry. FilmQA software was used to compare calculated and measured doses using the Gamma function. The region of interest (ROI) for analysis was defined as doses in excess of 20%, 30% or 40% of the maximum film dose. The average percentage of passing pixels (APP) over the 12 patients was scored for analysis. Sensitivity to source positional errors was evaluated by introducing errors of 1, 3, and 5 mm in one channel and quantifying the resultant change in APP. Results: The patient‐averaged data showed that an APP greater than 95% was achieved for the 2 cm film using criteria of 2%/3 mm with ROI=30%. For the 5 cm film an APP greater than 95% was achieved with criteria of 3%/3 mm with ROI =40%. Introducing positional errors greater than 1 mm resulted in drastic fall off in APP for all chosen ROIs and all DD/DTA criteria. For film at 2 cm and 2%/3 mm criteria, 2 mm shift error resulted in as much as 50% decrease in APP. Conclusion: The patient plans in this study were used to establish the use of 2%/3mm for gamma analysis in the 2 cm film plane on an ROI that incorporates doses in excess of 30% of the max film dose. In the 5 cm film plane, 3%/3mm for doses in excess of 40% of the max film dose is appropriate. Natural Sciences and Engineering; Research Council of Canada Contract No. 386009.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".