Prostatic displacement during extreme hypofractionated radiotherapy using volumetric modulated arc therapy (VMAT)
Bibliographic record
Abstract
BACKGROUND: To determine prostate displacement during extreme hypofractionated volume modulated arc radiotherapy (VMAT) using pre- and post-treatment orthogonal images with three implanted gold seed fiducial markers. METHODS: A total of 150 image pairs were obtained from 30 patients who underwent extreme hypofractionated radiotherapy to a dose of 40 Gy in five fractions on standard linear accelerators. Position verification was obtained with orthogonal x-rays before and after treatment and were used to determine intra-fraction prostate displacement. RESULTS: The mean prostate displacements were 0.03 ± 1.23 mm (1SD), 0.18 ± 1.55 mm, and 0.37 ± 1.95 mm in the left-right, superior-inferior, and anterior-posterior directions, respectively. The mean 3D displacement was 2.32 ± 1.55 mm. Only 6 (4%) fractions had a 3D displacement of >5 mm. The average time of treatment delivery for a given fraction was 195 ± 59 seconds. CONCLUSIONS: The mean intra-fraction prostate displacement during a course of extreme hypofractionated radiotherapy delivered via VMAT, continues to be small. Clinical margins typically used in a similar fixed-angle IMRT treatment are adequate. The use of VMAT in further extreme hypofractionation may limit prostatic motion uncertainties that would be otherwise be associated with longer treatment times.
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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 | 0.000 |
| 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) | 0.001 | 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".