SU‐E‐T‐624: Population Based Respiratory Analysis of Partial Breast IMRT and Impact of Breast Shape and Size
Bibliographic record
Abstract
Purpose: IMRT and partial breast irradiation (PBI) are used to improve cosmetic outcomes in early stage breast cancer patients; however, homogeneity and dose coverage are degraded by respiratory motion during delivery. We investigate the impact of respiratory motion on PBI IMRT using population respiratory data. Methods: The fluences from five different PBI IMRT plans were convolved with a population probability density function (PDF) to simulate dose delivery during respiration. Plan quality and coverage of the CTVsu (CTV plus 5 mm set‐up margin, trimmed back from skin and chestwall) were evaluated using DVH, hotspot, cold spot, and uniformity index. Results: All plans showed blurring (loss of coverage) in the shoulder region of the DVH curves for CTVsu and increased hotspot to the ipsilateral breast. For the patient with the largest breast volume and the smallest DEV:breast volume ratio, the CTVsu is surrounded by tissue. In this patient, the CTVsu coverage is slightly degraded and the hot spot becomes slightly hotter. The patients with a target volume near skin, lung or severe contour changes experience coverage loss near inhomogeneities and increased hot spots. This occurs for trimmed DEVs, although flash was added appropriately to all plans and coverage to the PTV (not only DEV) is adequate in the original plan. In patients with small breasts the CTVsu was noticeably hotter under respiratory conditions than static with the hot spot (2cc) in the CTVsu increasing by 7% and the 95% coverage increasing by 2%. Conclusions: The impact of respiratory motion on PBI is nontrivial and mainly patient dependent. Breast and target shape are the major determining factors in degradation of plan quality with respiration.
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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 | 0.003 |
| 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 | 0.000 | 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".