Sci—Thur AM: YIS ‐ 01: Dosimetric Analysis of Respiratory Induced Cardiac Intrafraction Motion in Left‐sided Breast Cancer Radiotherapy
Bibliographic record
Abstract
Introduction: Long‐term cardiac side effects in left‐sided breast cancer patients (BREL) after post‐operative radiotherapy has become one of the most debated issues in radiation oncology. Through breathing‐adapted radiotherapy the volume of the heart exposed to radiation can be significantly reduced by delivering the radiation only at the end of inspiration phase of the respiratory cycle, this is referred to as inspiration gating (IG). The purpose of this study is to quantify the potential reduction in cardiac exposure during IG compared to conventional BREL radiotherapy and to assess the dosimetric impact of cardiac motion due to natural breathing. Methods: 24 BREL patients treated with tangential parallel opposed photon beams were included in this study. All patients received a standard fast helical planning CT (FH‐CT) and a 4D‐CT. Treatment plans were created on the FH‐CT using a clinical treatment planning system. The original treatment plan was then superimposed onto the end of inspiration CT and all 10 phases of the 4D‐CT to quantify the dosimetric impact of respiratory motion and IG through 4D dose accumulation. Results: Through IG the mean dose to the heart, left ventricle, and left anterior descending artery (LAD) can be reduced in comparison to the clinical standard BREL treatment by as much as 8.39%, 10.11%, and 13.71% respectively (p < 0.05). Conclusion: Failure to account for respiratory motion can lead to under or overestimation in the calculated DVH for the heart, and it's sub‐structures. IG can reduce cardiac exposure especially to the LAD during BREL radiotherapy.
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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) | 0.008 | 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".