Attenuator design for organs at risk in total body irradiation using a translation technique
Bibliographic record
Abstract
Total body irradiation (TBI) is an efficient part of the treatment for malignant hematological diseases. Dynamic TBI techniques provide great advantages (e.g., dose homogeneity, patient comfort) while overcoming treatment room space restrictions. However, with dynamic techniques come additional organs at risk (OAR) protection challenges. In most dynamic TBI techniques, lead attenuators are used to diminish the dose received by the OARs. The purpose of this study was to characterize the dose deposition under various shapes of attenuators in static and dynamic treatments. This characterization allows for the development of a correction method to improve attenuator design in dynamic treatments. The dose deposition under attenuators at different depths in dynamic treatment was compared with the static situation based on two definitions: the coverage areas and the penumbra regions. The coverage area decreases with depth in dynamic treatment while it is stable for the static situation. The penumbra increases with depth in both treatment modes, but the increasing rate is higher in the dynamic situation. Since the attenuator coverage is deficient in the dynamic treatment mode, a correction method was developed to modify the attenuator design in order to improve the OAR protection. The correction method is divided in two steps. The first step is based on the use of elongation charts, which provide appropriate attenuator coverage and acceptable penumbra for a specific depth. The second point is a correction method for the thoracic inclination, which can introduce an orientation problem in both static and dynamic treatments. This two steps correction method is simple to use and personalized to each patient's anatomy. It can easily be adapted to any dynamic TBI techniques.
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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 | 0.001 |
| 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) | 0.001 | 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".