Sci‐Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques ‐ 04: Assessment of intra‐fraction motion during lung SABR VMAT using a custom abdominal compression device
Bibliographic record
Abstract
Purpose: Lung SABR patients are treated using Volumetrically Modulated Arc Therapy (VMAT), utilizing 2 arcs with Conebeam CT (CBCT) image‐guidance prior to each arc. Intra‐fraction imaging can prolong treatment time (up to 20%), and the aim of this study is to determine if it is necessary. Methods: We utilize an in‐house abdominal compression device to minimize respiratory motion, 4DCT to define the ITV, a 5 mm PTV margin and a 2–3 mm PRV margin. We treated 23 patients with VMAT, fifteen were treated to 48 Gy in 4 fractions, while eight were treated with up to 60 Gy in 8 fractions. Intrafraction motion was assessed by the translational errors recorded for the second CBCT. Results: There was no significant difference (t‐test, p=0.93) in the intra‐fraction motion between the patients treated with 4 and 8 fractions, or between the absolute translations in each direction (ANOVA, p=0.17). All 124 intra‐fraction CBCT images were analysed and 95% remained localized within the 5 mm PTV margin The mean magnitude of the vector displacement was 1.8 mm. Conclusions: For patients localized with an abdominal compression device, the intrafraction CBCT image may not be necessary, if it is only the tumor coverage that is of concern, as the patients are typically well within the 5 mm PTV margin. On the other hand, if there is a structure with a smaller PRV margin, an intrafraction CBCT is recommended to ensure that the dose limit for the organ at risk is not exceeded.
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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".