SU‐E‐T‐658: Calculation of the Prostate Equivalent Uniform Dose for Interfraction Organ Motion Using the Gaussian Error Function Model
Bibliographic record
Abstract
Purpose: The Gaussian error function (GEF) model was used to fit into the dose‐volume histograms (DVHs) of prostate IMRT and calculate the prostate equivalent uniform dose (EUD) associated with interfraction organ motion. Methods: Three patients with small (39 cc), medium (60 cc) and large (87 cc) prostate volume were selected from a group of twenty in this study. Cumulative DVHs for the prostate that were 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. To simulate the interfraction prostate motion, the prostate was shifted 1 cm in the anterior and posterior directions in 2 mm steps, using the dose distribution based on the IMRT plan without actual prostate motion. The prostate cumulative DVHs were converted to corresponding differential DVHs to calculate the prostate EUDs in each interfraction motion using MATLAB. Results: Prostate EUD was computed to per fraction in order to measure the equivalent dose at each movement step. Prostate EUDs were found to decrease as the prostate shifted to both the anterior and posterior directions. The prostate EUD was also expected to have the maximum value at the isocentre, since the target received the best dose coverage when the prostate displacement remains zero. Our result showed that patient with the smallest prostate volume (39 cc) in the group has the smallest prostate EUD, when the prostate was shifted 1 cm anteriorly and posteriorly. Since the percentage difference of the prostate EUD calculated by the TPS and GEF is significantly less than 0.5%, we consider the GEF model a good potential alternative to determine the prostate EUD. Conclusions: We validated our GEF model which can predict the prostate EUD with accuracy less than 0.5% compared to the results from the TPS.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".