SU‐E‐T‐322: The Effects of Microsphere and Surrounding Material Composition on Y‐90 Dose Kernels
Bibliographic record
Abstract
Purpose: To investigate the effects of microsphere and surrounding material composition on Y‐90 dose kernels. Methods: Dose kernels can be convolved with activity concentrations to calculate non‐uniform dose distributions for patient‐specific dosimetry of Y‐90 microsphere treatments. Monte Carlo simulations were completed with EGSnrc user code EDKnrc to calculate the dose rate at multiple radial distances around various Y‐90 microsphere sources. Glass and resin microsphere simulations were completed with average diameter and density values as well as maximum values based on those given in literature. Both water and liver (as defined in ICRU44, density of 1.06g/cm̂3) were used as the surrounding/scoring material. Point source simulations in water were compared to ICRU72 reference data to validate the simulations. Point source simulations were also completed in water with a density of 1.06g/cm̂3 to evaluate the effects of the density of the surrounding material. All simulations had statistical uncertainties less than 1%.Results: Point source simulations agree to within 2% of the ICRU72 reference data over the range investigated. Glass and resin microsphere simulations in water show a slight decrease in dose rate relative to ICRU72 near the maximum range of Y‐90. The maximum differences were −3.1% and −1.9%, respectively. Simulations in liver show large differences relative to ICRU72, approaching 60% near the maximum range of Y‐90. However, this deviation can be mostly attributed to the difference in density between water and liver Conclusions: The presence of microsphere material causes slight differences in the dose kernel near the maximum range of Y‐90. Large differences were seen in simulations in liver relative to those in water. This is mostly attributed to differences in density of the materials. For accurate patient‐specific dosimetry, it would be necessary to take these differences into account in the calculation of delivered dose.
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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".