Intrafraction Prostate Motion Management for Ultra-Hypofractionated Radiotherapy of Prostate Cancer
Bibliographic record
Abstract
Purpose: Determine the time-dependent magnitude of intrafraction prostate displacement and a cutoff for the tracking decision. Methods: Nine patients with localized prostate cancer were treated with ultra-hypofractionated radiotherapy (CyberKnife) with fiducial markers. Exact tract kV/kV imaging was used with an average interval of 19−92 s. A Gaussian distribution was calculated for the x-, y-, and z-directions (σx,y,z). The variation of prostate motion (μσ) was obtained by averaging the patients’ specifics, and the safety margin was calculated to be MAB = WYm + WBSs. Results: The calculated PTV safety margins were as follows: at 40 s: 0.55 mm (L/r), 0.85 mm (a/p), and 1.05 mm (s/i); at 60 s: 0.9 mm (L/r), 1.35 mm (a/p), and 1.55 mm (s/i); at 100 s: 1.5 mm (L/r), 2.3 mm (a/p), and 2.6 mm (s/i); at 150 s: 1.9 mm (L/r), 3.1 mm (a/p), and 3.6 mm (s/i); at 200 s: 2.2 mm (L/r), 3.8 mm (a/p), and 4.2 mm (s/i); and at 300 s: 2.6 mm (L/r), 5.3 mm (a/p), and 5.6 mm (s/i). A tracking cutoff of 2.5 min seemed reasonable. In order to achieve an accuracy of < 1 mm, tracking with < 50 s intervals was necessary. Conclusions: For ultra-hypofractionated radiotherapy of the prostate with treatment times > 2.5 min, intrafraction motion management is recommended.
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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) | 0.000 | 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".