HyperArc brain SRS target coverage variation due to intra-fractional motion for multiple brain lesions of varying separations and sizes
Bibliographic record
Abstract
Abstract Purpose: An evaluation was conducted for HyperArc brain stereotactic radiosurgery (SRS) plans to determine the effect of intra-fractional motion on target coverage and dosimetry to understand the safe limits of zero-PTV-margin HyperArc SRS treatments. Material and methods: The Steve phantom was CT scanned with an Encompass mask and imported into the Eclipse Treatment Planning System, version 16·01. Two high-resolution spherical contours (GTV1 and GTV2) were generated with various sizes and separations. The rotational shift was transformed into translation based on the distance from the tumours to the isocentre and the rotational angle between the tumours. The effect of translational shifts corresponding to each rotational shift on dosimetric performance was evaluated for various tumour sizes and distances to the isocentre. Results: The tumour coverage was compared for different tumour sizes and distances to the isocentre due to intra-fractional motion. For tumour sizes less than 6 mm in diameter, the volume coverage changes significantly for separations greater than 5·5 cm from the isocentre. The effects of intra-fractional motion on dosimetry dominate for separations greater than 5·5 cm. Even though the percent volume coverage changes sharply for extreme shifts, the percent dose coverage stays between 70 and 80% for extreme shifts (1·2° in our case) for all lesion sizes. Conclusions: The target-to-isocentre separation and the size of the lesions are two major factors that contribute to significant dosimetric deviations. When the target-to-isocentre distance is within 3·5 cm, D100% is over 90% coverage. Over 90% target coverage is achievable for zero PTV margin in situations where extreme shifts of 1·2° exist.
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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) | 0.001 | 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".