Poster ‐ 45: The effect of plan modulation on VMAT liver SBRT treatments: A motion interplay study
Bibliographic record
Abstract
Purpose: To investigate whether increasing the degree of MLC leaf modulation in VMAT liver SBRT treatment plans increases the dose differences due to the interplay effect. Methods: Two VMAT plans, delivering 54Gy in three fractions, with differing degrees of MLC aperture modulation were created for each of 10 patients. To simulate respiratory motion, an in‐house program was used to shift the positions of each active MLC leaf at every 0.6° of gantry rotation, according to the amplitude of a respiratory trace. To isolate the interplay effect from dose blurring, motion was simulated using four different starting points in the respiratory cycle. The same starting point was used for each of the three fractions, representing a worst case scenario. The four resultant dose distributions from each plan were subtracted from each other, and dose differences in the GTV were quantified using the standard deviation of the differential DVH. Results: Dose differences up to 1Gy were found in the GTV of the dose subtractions, indicating the presence of interplay effects. A Wilcoxon Signed Rank test indicates a significant (p<0.05) increase in the standard deviations of the low‐modulation plans to those with a higher degree of modulation. No planning constraints were exceeded with the introduction of respiratory motion. Conclusions: VMAT liver SBRT plans with a high degree of modulation exhibit an increased susceptibility to interplay effects. The dose differences due to interplay are not large enough to markedly decrease the plan quality.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".