SU-GG-T-594: 3D Heterogeneous Dose Distributions for Total Body Irradiation Patients
Bibliographic record
Abstract
Purpose: One major objective of Total Body Irradiation (TBI) treatments is to deliver a uniform dose in the entire body of the patient. Looking at 3D dose distributions for constant speed (CteSpeed) and variable speed (VarSpeed) translating couch TBI treatments, dose uniformity and the effect of body heterogeneities were evaluated. Method and Materials: This study was based on retrospective dose calculations of 10 patients treated with a translating couch TBI technique. Dose distributions for CteSpeed and VarSpeed TBI treatments have been computed with Pinnacle3 treatment planning system in homogeneous (Homo) and heterogeneous (Hetero) dose calculation modes. A specific beam model was implemented in Pinnacle3 to allow an accurate dose calculation adapted for TBI special aspects. Results: Better dose coverages were obtained with Homo/VarSpeed treatments compared to Homo/CteSpeed cases including smaller overdosage areas. Large differences between CteSpeed and VarSpeed dose calculations were observed in the brain, spleen, arms, legs, and lateral parts of the abdomen (differences between V100% mean values up to 57.5%). Results also showed that dose distributions for patients treated with CteSpeed TBI greatly depend on the patient morphology, especially for pediatric and overweight cases. Looking at heterogeneous dose calculations, overdosages (5–15%) were found in low density regions (i.e bones), while overdosages (5–15%) were found in low density regions (i.e lungs). Overall, Homo/CteSpeed and Hetero/VarSpeed dose distributions showed more hot spots than Homo/VarSpeed and they were greatly dependent on the patient anatomy. Conclusion: CteSpeed TBI treatments allow a simple optimization process but lead to less dose uniformity due to the patient anatomy. VarSpeed TBI treatments require more complex dose optimization, but lead to a better dose uniformity independent of the patient morphology. Finally, this study showed that heterogeneities should be considered in dose calculations in order to obtain a better optimization and therefore, to improve dose uniformity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".