SU-E-J-62: Breath Hold for Left-Sided Breast Cancer: Visually Monitored Deep Inspiration Breath Hold Amplitude Evaluated Using Real-Time Position Management
Bibliographic record
Abstract
Purpose: We used Real-Time Position Management (RPM) to evaluate breath hold amplitude and variability when gating with a visually monitored deep inspiration breath hold technique (VM-DIBH) with retrospective cine image chest wall position verification. Methods: Ten patients with left-sided breast cancer were treated using VM-DIBH. Respiratory motion was passively collected once weekly using RPM with the marker block positioned at the xiphoid process. Cine images on the tangent medial field were acquired on fractions with RPM monitoring for retrospective verification of chest wall position during breath hold. The amplitude and duration of all breath holds on which treatment beams were delivered were extracted from the RPM traces. Breath hold position coverage was evaluated for symmetric RPM gating windows from ± 1 to 5 mm centered on the average breath hold amplitude of the first measured fraction as a baseline. Results: The average (range) breath hold amplitude and duration was 18 mm (3–36 mm) and 19 s (7–34 s). The average (range) of amplitude standard deviation per patient over all breath holds was 2.7 mm (1.2–5.7 mm). With the largest allowable RPM gating window (± 5 mm), 4 of 10 VM-DIBH patients would have had ≥ 10% of their breath hold positions excluded by RPM. Cine verification of the chest wall position during the medial tangent field showed that the chest wall was greater than 5 mm from the baseline in only 1 out of 4 excluded patients. Cine images verify the chest wall/breast position only, whether this variation is acceptable in terms of heart sparing is a subject of future investigation. Conclusion: VM-DIBH allows for greater breath hold amplitude variability than using a 5 mm gating window with RPM, while maintaining chest wall positioning accuracy within 5 mm for the majority of 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 | 0.001 | 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) | 0.000 | 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".