Poster ‐ 55: Active Breathing Coordinator Based Treatment of Liver SBRT Patients
Bibliographic record
Abstract
Purpose: Accuracy of treatment delivery for liver SBRT patients can be compromised by breathing induced motion. Recently we started using active breathing coordinator (ABC) to “freeze” the breathing motion. This work describes our initial experience with ABC based treatment of liver SBRT patients. Methods: Patients are treated in maximum exhale state with a minimum required breath hold time of 20 s. Fluoroscopy is used to assess diaphragm stability before both simulation and treatment. Depending on the proximity of organs at risk, 30–60 Gy are given in five fractions every other day and delivered via VMAT. CBCT is used for patient setup verification with robotic couch compensating for translations/rotations. An additional CBCT is acquired after every fraction to confirm patient's stability during treatment. Results: Six patients have successfully been treated using the ABC protocol so far with an approximate treatment time of 1 h. CBCT acquired after the treatment suggests that patients are stable and the liver position, when locked by ABC, is reproducible throughout the treatment (average deviation 1.9 mm). The major immediate benefit of using ABC is a drastic improvement in image quality of the CT simulation as well as CBCT images. Conclusions: ABC eliminates breathing motion and, as such, substantially improves the quality of the images acquired at CT simulation as well as CBCT images leading to more reliable dose delivery. The position of the liver remains stable for the duration of treatment when using the ABC system. The treatment is well tolerated by the patients.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".