Poster ‐ 33: Dosimetry Comparison of Prone Breast Forward and Inverse Treatment planning considering daily setup variations
Bibliographic record
Abstract
Introduction: The purpose of this study is to investigate the effects of daily setup variations on prone breast forward field‐in‐field (FinF) and inverse IMRT treatment planning. Methods: Rando Phantom (Left breast) and Pixy phantom (Right breast) were built and CT scanned in prone position. The treatment planning (TP) is performed in Eclipse TP system. Forward FinF plan and inverse IMRT plan were created to satisfy the CTV coverage and OARs criteria. The daily setup variations were assumed to be 5 mm at left‐right, superior‐inferior, and anterior‐posterior directions. The DVHs of CTV coverage and OARs were compared for both forward FinF plan and inverse IMRT plans due to 5mm setup variation. Results and Discussions: DVHs of CTV coverage had fewer variations for 5m setup variation for forward FinF and inverse IMRT plan for both phantoms. However, for the setup variations in the left‐right direction, the DVH of CTV coverage of IMRT plan showed the worst variation due to lateral setup variation for both phantoms. For anterior‐posterior variation, the CTV could not get full coverage when the breast chest wall is shallow; however, with the guidance of MV imaging, breast chest wall will be checked during the MV imaging setup. So the setup variations have more effects on inverse IMRT plan, compared to forward FinF plan, especially in the left‐right direction. Conclusions: The Forward FinF plan was recommended clinically considering daily setup variation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".