TU‐F‐CAMPUS‐J‐01: Inference of Prostate PTV Margins in VMAT Delivery From Intra‐Fraction Prostate Motion During SBRT Delivery
Bibliographic record
Abstract
Purpose: To retrospectively quantify the intra‐fraction prostate motion during stereotactic body radiation therapy (SBRT) treatment using CyberKnife's target tracking system, which may provide insight into expansion margins from GTV to PTV used in gantry‐based treatments. CyberKnife is equipped with an active tracking system (InTempo) that tracks the four fiducials placed in the prostate gland. The system acquires intra‐fraction orthogonal kV images at 45° and 315° in a sequential fashion. Methods: A total of 38 patients treated with SBRT using CyberKnife between 2011 and 2013 were studied. Dose‐regime was 36.25 Gy in 5 fractions (7.25 Gy/fraction, twice per week) as per RTOG 0938 guidelines. The CyberKnife image tracking logs for all SBRT treatments using InTempo were examined. A total of 13663 images were examined for the superior/inferior (SI), anterior/posterior (AP) and left/right (LR) translation as well as roll, pitch and yaw rotations for the target position relative to the last known model position. Results: The mean ± 2 SD of intra‐fraction motion was contained within 3 mm for SI and LR and 4.5 mm for AP directions at 5 minutes into the treatment delivery. It was contained within 4 mm for SI and LR and 5 mm for AP at 10 minutes. At 15 minutes into delivery, all translations were contained within 5 mm. The mean ± 2 SD of prostate roll, pitch and yaw increased with time but were contained within 5 degree at 5, 10 and 15 minutes into treatment. Additionally, target translations and rotations were within ± 1 mm and ± 1 degree for 90% and 78% of the time. Conclusion: The organ motion component of PTV margin for 10 minute VMAT delivery is contained within 4 mm in SI and LR direction and within 5 mm in the AP direction.
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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) | 0.003 | 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".