Evaluation of target and cardiac position during visually monitored deep inspiration breath‐hold for breast radiotherapy
Bibliographic record
Abstract
A low-resource visually monitored deep inspiration breath-hold (VM-DIBH) technique was successfully implemented in our clinic to reduce cardiac dose in left-sided breast radiotherapy. In this study, we retrospectively characterized the chest wall and heart positioning accuracy of VM-DIBH using cine portal images from 42 patients. Central chest wall position from field edge and in-field maximum heart distance (MHD) were manually measured on cine images and compared to the planned positions based on the digitally reconstructed radiographs (DRRs). An in-house program was designed to measure left anterior descending artery (LAD) and chest wall separation on the planning DIBH CT scan with respect to breath-hold level (BHL) during simulation to determine a minimum BHL for VM-DIBH eligibility. Systematic and random setup uncertainties of 3.0 mm and 2.6 mm, respectively, were found for VM-DIBH treatment from the chest wall measurements. Intrabeam breath-hold stability was found to be good, with over 96% of delivered fields within 3 mm. Average treatment MHD was significantly larger for those patients where some of the heart was planned in the field compared to patients whose heart was completely shielded in the plan (p < 0.001). No evidence for a minimum BHL was found, suggesting that all patients who can tolerate DIBH may yield a benefit from it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".