Breast Volume Is a Predictor of Higher Heart Dose in Whole-Breast Supine Free-Breathing Volumetric-Modulated Arc Therapy Planning
Bibliographic record
Abstract
In breast cancer volumetric-modulated arc therapy (VMAT) planning, the rotation of the gantry around the target implies a greater dose spreading to the whole heart, compared to tangential-field standard treatment. A consecutive cohort of 121 breast cancer patients treated with the VMAT technique was investigated. The correlation of breast volume, heart volume and lung volume with mean heart dose (mHD) and mean and maximum LAD dose (mLAD dose, MLAD dose) was tested, and a subsequent a linear regression analysis was carried out. VMAT treatment plans from 56 left breast cancer and 65 right breast cancer patients were analyzed. For right-sided patients, breast volume was significantly correlated with mHD, mLAD and MLAD dose, while for left-sided patients, breast volume was significantly correlated with mHD and mLAD, while heart volume and lung volume were correlated with mHD, mLAD and MLAD dose. Breast volume was the only predictor of increased heart and LAD dose (p ≤ 0.001) for right-sided patients. In left-sided patients, heart and lung were also predictors of increased mHD (p = 0.005, p ≤ 0.001) and mean LAD dose (p = 0.009, p ≤ 0.001). In this study, we observed an increase in heart and LAD doses in larger-breasted patients treated with VMAT planning. In right-sided patients, breast volume was shown to be the only predictor of increased heart dose and LAD dose.
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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 | 0.002 |
| 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".