Poster — Thur Eve — 76: A quality control to achieve planning consistency in arc radiotherapy of the prostate
Bibliographic record
Abstract
PURPOSE: To report a quality control program in prostate radiation therapy at our center that includes semi-automated planning process to generate high quality plans and in-house software to track plan quality in the subsequent clinical application. MATERIAL AND METHODS: Arc planning in Eclipse v10.0 was preformed for both intact prostate and post-prostatectomy treatments. The planning focuses on DVH requirements and dose distributions being able to tolerate daily setup variations. A modified structure set is used to standardize the optimization, including short rectum and bladder in the fields to effectively tighten dose to target and a rectum expansion with 1cm cropped from PTV to block dose and shape posterior isodose lines. Structure, plan and optimization templates are used to streamline plan generation. DVH files are exported from Eclipse to a quality tracking software with GUI written in Matlab that can report the dose-volume data either for an individual patient or over a patient population. RESULTS: For 100 intact prostate patients treated with 78Gy, rectal D50, D25, D15 and D5 are 30.1±6.2Gy, 50.6±7.9Gy, 65.9±6.0Gy and 76.6±1.4Gy respectively, well below the limits 50Gy, 65Gy, 75Gy and 78Gy respectively. For prostate bed with prescription of 66Gy, rectal D50 is 35.9±6.9Gy. In both sites, PTV is covered by 95% prescription and the hotspots are less than 5%. CONCLUSION: The semi-automated planning method can efficiently create high quality plans while the tracking software can monitor the feedback from clinical application. It is a comprehensive and robust quality control program in radiation therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".