Evaluation of cardiac dose reduction with deep inspiration breath hold in patients with left-sided breast cancer receiving adjuvant radiotherapy.
Bibliographic record
Abstract
1096 Background: Studies have suggested increased cardiac morbidity from radiation exposure to the heart and left anterior descending artery (LAD) in breast cancer patients receiving adjuvant radiotherapy (RT). Deep inspiration breath hold (DIBH) techniques have demonstrated reduction to heart, LAD and lung dose in left-sided breast cancers. There is, however, limited data on which patients derive most benefit from DIBH technique. Objective: To compare reduction in cardiac and LAD doses using a DIBH technique in left-sided breast cancer patients treated with adjuvant RT to the breast alone versus those also receiving regional nodal RT Methods: Twenty consecutive patients with left-sided breast cancer underwent CT simulation in free breathing (FB) and DIBH. Patients were grouped into two cohorts: those receiving whole breast RT alone +/- boost (WBRT) versus whole breast/chest wall RT with regional nodal irradiation (WBRT + RNI). 3D conformal plans were devised, and dosimetric comparisons were made between the two techniques for each cohort. Results: Eleven patients received WBRT while nine patients received WBRT + RNI. All patients had comparable CTV coverage on both DIBH and FB treatment plans. Mean heart and LAD doses were lower in all DIBH versus FB plans in both groups, but the benefit was larger in the group receiving RNI compared to those receiving WBRT alone (average relative reduction in mean heart and LAD dose: 55.9% and 71.9% vs 34.2% and 45.1%, respectively). All patients met a mean heart dose of <4Gy on DIBH. On FB, only one patient in the WBRT group did not meet this constraint, compared to five patients in the WBRT +RNI group. Conclusions: Patients receiving WBRT+RNI had a greater reduction in heart and LAD dose from DIBH than patients receiving WBRT alone. The majority of patients receiving WBRT met a mean heart dose of <4Gy on FB planning, while less than half of patients receiving WBRT + RNI were able to meet this constraint. These findings suggest a greater benefit from DIBH treatment in patients receiving RNI, while patients receiving WBRT alone may be safely treated with a FB technique. Ongoing prospective cohorts are being evaluated to ensure validity of these findings.
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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) | 0.001 | 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".