Predicting intrafraction displacement of the prostate during a five-fraction radiotherapy regimen: Implications for stereotactic body radiotherapy.
Bibliographic record
Abstract
90 Background: Accurate characterization of prostate displacement during stereotactic body radiotherapy (RT) may help optimize margins to maximize complication and disease-free survival. Also, if individual patients with large intrafraction displacement can be identified, additional interventions can be undertaken. Methods: Fifty-three men treated on a phase I/II study of extreme hypofractionation were selected for the study. A total dose of 35 Gy in 5 fractions was delivered with intensity modulated radiotherapy (IMRT). Daily image guidance was performed using gold seed fiducials. Position verification was obtained with orthogonal electronic portal images taken immediately before and after each fraction. Prostate shifts were recorded in 3 dimensions. Results: The mean intrafraction prostate displacements were -0.03 ± 0.61 mm (1SD), 0.21 ± 1.50 mm, and -0.86 ± 1.73 mm in the lateral, superior-inferior, and anterior-posterior directions, respectively. The mean intrafraction displacement during the first two fractions is moderately correlated with the displacement in the remaining 3 fractions, with correlation coefficients of 0.63 and 0.47 in the SI and AP directions, respectively. Conclusions: The mean intrafraction prostate displacement during a course of extreme hypofractionated radiotherapy is small. A strategy using the first two fractions to predict future displacements >5mm shows promise and warrants further validation. No significant financial relationships to disclose.
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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) | 0.000 | 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".