Sci—Thur PM: Planning & Delivery — 04: Respiratory margin derivation and verification in partial breast irradiation
Bibliographic record
Abstract
Partial breast irradiation (PBI) following breast‐conserving surgery is emerging as an effective means to achieve local control and reduce irradiated breast volume. Patients are planned on a static CT image; however, treatment is delivered while the patient is free‐breathing. Respiratory motion can degrade plan quality by reducing target coverage and/or dose homogeneity. A variety of methods can be used to determine the required margin for respiratory motion in PBI. We derive geometric and dosimetric respiratory 1D margin. We also verify the adequacy of the typical 5 mm respiratory margin in 3D by evaluating plan quality for increasing respiratory amplitudes (2–20 mm). Ten PBI plans were used for dosimetric evaluation. A database of volunteer respiratory data, with similar characteristics to breast cancer patients, was used for this study. We derived a geometric 95%‐margin of 3 mm from the population respiratory data. We derived a dosimetric 95%‐margin of 2 mm by convolving 1D dose profiles with respiratory probability density functions. The 5 mm respiratory margin is possibly too large when 1D coverage is assessed and could lead to unnecessary normal tissue irradiation. Assessing margins only for coverage may be insufficient; 3D dosimetric assessment revealed degradation in dose homogeneity is the limiting factor, not target coverage. Hotspots increased even for the smallest respiratory amplitudes, while target coverage only degraded at amplitudes greater than 10 mm. The 5 mm respiratory margin is adequate for coverage, but due to plan quality degradation, respiratory management is recommended for patients with respiratory amplitudes greater than 10 mm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.021 | 0.007 |
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".