SU‐D‐201‐02: A Systematic Planning Quality Monitoring Program for VMAT Treatment
Bibliographic record
Abstract
Purpose: To demonstrate a systematic tracking program for planning quality; to advocate that commercial treatment planning systems develop tools to monitor population‐based quality in order to justify technology change in respect to clinical outcome. Methods: The DVH (Dose Volume Histogram) files of VMAT patients since 2011 have been exported from Varian Eclipse treatment planning system. The nomenclature of structures has been standardized and the planning process has been semi‐automated for clinical sites where such efforts bring clinical and operational benefits, such as GU. An in‐house program was developed in C++ to extract the dose volume points of targets and organs at risk from the exported DVH files. The control points were determined by QUANTEC (Quantitative Analyses of Normal Tissue Effects in the Clinic) organ specific dose volume recommendations for toxicity control. Monitor units were used as the indication of treatment efficiency. Control charts for dose volume points and monitor units were created to monitor planning quality dynamically. Results: The figures in the supporting document show some examples of prostate 78Gy plans over the most recent 30 months. The contoured CTV, rectum and bladder are statistically stable at 74±27 cm3, 77±28cm3 and 366±165cm3, respectively, reflecting consistence in target delineation, bowel and bladder preparation. Rectum V50, V60, V65, V70 and V75 remain at 24±6 %, 18±5%, 16±5%, 13±4 % and 9±4%, respectively, well below QUANTEC recommendations of 50%, 30%, 25%, 20% and 15%. The stability is also observed in targets and other OARs’ doses. However, monitor units have increased by 11%, from 533±42MU to 594±40MU, after Eclipse version 11 upgrade in which the optimization algorithm was changed. Conclusion: Monitoring population‐wise quality provides an assessment of technology changes and clinical outcomes, which can help guide future development. We therefore recommend these types of tools be incorporated in commercial treatment planning systems.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".