Poster - Thurs Eve-13: Modeling the effect of organ motion on cumulative rectal dose using EUD
Bibliographic record
Abstract
A rigid body model and electronic serial portal imaging (EPI) data were used to generate the cumulative dose distribution for the rectum incorporating organ motion during IMRT. The impact of rectal motion was assessed via NTCP and TCP based on equivalent uniform dose per fraction (EUDf). The rectal positional variations were measured fraction-to-fraction from MV EPI for 20 prostate patients implanted with gold seeds. Five-field (5F) and seven-field (7F) IMRT plans for prostate patients were constructed with prescribed dose 78 Gy/39 fractions using a Pinnacle3 treatment planning system. EUD increased in 45% of the patients with greater than 2.5% increase for 5% of the patients. While EUD decreased in 55% of the patients with greater than 2.5% decrease for 10% of the patients. The amplitudes of EUDf increase and decrease are correlated with the dose gradient. Higher dose gradients lead to higher rectal EUDf change. The rectal NTCP decreases for half of the patients and increases for the other for both 5F and 7F plans. The NTCP decreased with the increasing of dose gradient between the prostate and rectum for 5F and 7F IMRT plans. The correlation coefficient for the rectal NTCP and the dose gradient is − 0.71. The increase or decrease of rectal cumulative dose depends on the dose gradient, motion amplitude and frequency in AP direction. EUDf is a useful QA parameter for interpreting the biological impact of geometric uncertainties on the static dose distribution. Rectal NTCP is patient dependent and must be determined individually.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".