SU‐E‐T‐652: Rectal Equivalent Uniform Dose Analysis on the Prostate IMRT for Interfraction Organ Motion Using the Gaussian Error Function
Bibliographic record
Abstract
Purpose: The aim of this study is to verify the calculation of rectal equivalent uniform dose (EUD) using the Gaussian error function (GEF), which can reduce the dose‐volume database to enhance the computing efficiency. Methods: For a group of 20 prostate patients, cumulative dose‐volume histograms (cDVHs) for the rectum, shifted in the anterior—posterior directions based on a 7‐beam IMRT plan, were calculated and modeled using the Pinnacle3 treatment planning system (TPS) and GEF, respectively. The range of rectal shift for each patient was measured by daily electronic portal imaging with fiducial gold markers in the prostate. The shape of cDVH curve was fitted into the GEF model, and then the cDVHs were converted to corresponding differential DVHs to calculate the rectal EUDs in each interfraction organ motion. Results: Rectal EUD was calculated per fraction to determine the equivalent dose at each movement step. Our results showed that rectal EUDs increased as the rectum shifted to anterior direction and decreased in the posterior direction. Moreover, it is found that rectal EUDs have a negative linear relationship with respect to the organ displacement. Based on the results, the patient with the smallest prostate (volume = 40 cc) in the group received the highest rectal EUD when the rectum shifted 8 mm anterior. Percentage difference of rectal EUD using the TPS and GEF has been calculated for each movement. Since the results are less than 0.5%, the GEF model is considered a good potential alternative of the TPS. Conclusions: We concluded that the calculation of rectal EUD using the GEF model was validated with error less than 0.5%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".