Initial Experience of Implementing a Pre-treatment Dry Run for HyperArc Stereotactic Radiosurgery Treatments With Optical Surface Imaging for Intra-fraction Motion Monitoring
Bibliographic record
Abstract
Linac-based stereotactic radiosurgery (SRS) with planning target volume (PTV) margins <1 mm has become increasingly common in recent years. Optical surface imaging for surface-guided radiation therapy (SGRT) is often used for intra-fraction motion monitoring during these treatments to facilitate the use of a smaller PTV margin by providing real-time quantitative patient positioning information. However, rotating the couch introduces errors to SGRT-reported translations and rotations that can be problematic for SRS treatments with non-coplanar arcs and very small PTV margins. This work presents a novel approach for decreasing the magnitude of these errors by performing a pre-treatment dry run and capturing reference surfaces with the SGRT system at each couch angle included in the treatment plan. Time from cone beam computed tomography (CBCT) to treatment initiation and total treatment session time were reviewed for 30 single-fraction brain SRS cases treated using this technique to determine the effect of including the dry run on treatment session times. Out of the 30 cases treated between April 2023 and January 2024, 23 treatments required only a single CBCT prior to treatment, with no additional mid-treatment imaging required to verify patient positioning after motion. The median time between CBCT and treatment initiation was 7.98 minutes (interquartile range (IQR) = 7.28 to 8.93 minutes). The median time from CBCT to treatment completion was 15.43 minutes (IQR = 13.67 to 21.97 minutes). In the six patients that required one additional CBCT, the treatment session times ranged from 24.32 to 32.83 minutes. There was one patient who required three mid-treatment CBCTs, and the treatment session time was 67.87 minutes. Incorporating the pre-treatment dry run with the acquisition of reference surfaces at each treatment angle decreased errors in SGRT-reported translations and rotations associated with couch rotation without significantly increasing treatment session 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".