Poster ‐ 54: Development and Evaluation of Normal Tissue Objective Parameters and Avoidance Regions for Prostate Bed VMAT Treatments
Bibliographic record
Abstract
Purpose: A retrospective study was conducted to determine optimal NTO parameters on five patients that were treated with radical prostate doses of 66 Gy in 33 fractions to the prostate bed. The purpose of this work is to evaluate the effectiveness of various NTO settings and also provide insight into whether the use of multiple sparing techniques is beneficial or redundant for a given site. Methods: The effect of varying the NTO parameters were systematically examined in conjunction with the option of an avoidance region: a “ring” structure around the planning target volume. Within each of these groups, four plans were made; three with varying NTO settings as well as a fourth control plan not using the NTO. The three NTO settings used were the Varian default values, the automatic setting (a selectable option in the NTO function), and a developed set of test parameters (Manual NTO). Results: The site‐specific NTO plans showed the greatest improvement regardless of whether they were used in combination with the ring structure. The most striking improvement in OAR sparing occurred when using the Manual NTO settings without the ring structure. In this setting, the average reduction in mean bladder dose was 473 cGy. The automatic NTO and Varian default settings produced improvements of 146 cGy and 12 cGy, respectively. Conclusion: The use of site‐specific NTO parameters produced plans with marked improvement to normal tissue sparing in comparison to plans using the Varian default settings or the automatic NTO.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".