Sci‐Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques ‐ 06: Patient‐specific QA Procedure for Gated VMAT SABR Treatments using 10x Beam in Flattening‐Filter Free Mode
Bibliographic record
Abstract
Purpose: To develop a highly sensitive patient specific QA procedure for gated VMAT stereotactic ablative radiotherapy (SABR) treatments. Methods: A platform was constructed to attach the translational stage of a Quasar respiratory motion phantom to a pinpoint ion chamber insert and move the ion chamber inside the ArcCheck. The Quasar phantom controller uses a patient‐specific breathing pattern to translate the ion chamber in a superior‐inferior direction inside the ArcCheck. With this system the ion chamber is used to QA the correct phase of the gated delivery and the ArcCheck diodes are used to QA the overall dose distribution. This novel approach requires a single plan delivery for a complete QA of a gated plan. The sensitivity of the gating QA procedure was investigated with respect to the following parameters: PTV size, exhale duration, baseline drift, gating window size. Results: The difference between the measured dose to a point in the penumbra and the Eclipse calculated dose was under 2% for small residual motions. The QA procedure was independent of PTV size and duration of exhale. Baseline drift and gating window size, however, significantly affected the penumbral dose measurement, with differences of up to 30% compared to Eclipse. Conclusion: This study described a highly sensitive QA procedure for gated VMAT SABR treatments. The QA outcome was dependent on the gating window size and baseline drift. Analysis of additional patient breathing patterns is currently undergoing to determine a clinically relevant gating window size and an appropriate tolerance level for this procedure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".