Determining the effects of microsphere and surrounding material composition on <sup>90</sup>Y dose kernels using <scp>egsnrc</scp> and <scp>mcnp5</scp>
Bibliographic record
Abstract
PURPOSE: Recent advances in the imaging of (90)Y using positron emission tomography (PET) and improved uncertainty in the branching ratio for the internal pair production component of (90)Y decay allow for a more accurate determination of the activity distribution of (90)Y microspheres within a patient. This improved activity distribution can be convolved with the dose kernel of (90)Y to calculate the dose distribution within a patient. This work investigates the effects of microsphere and surrounding material composition on (90)Y dose kernels using egsnrc and mcnp5 and compares the results of these two transport codes. METHODS: Monte Carlo simulations were performed with egsnrc and mcnp5 to calculate the dose rate at multiple radial distances around various (90)Y sources. Point source simulations were completed with mcnp5 to determine the optimal electron transport settings for this work. After determining the optimal settings, point source simulations were completed using egsnrc (user code edknrc) and mcnp5 in water and liver [as defined by the International Commission on Radiation Units and Measurements (ICRU) Report 44]. The results were compared to ICRU Report 72 reference data. Point source simulations were also completed in water with a density of 1.06 g[middle dot]cm(-3) to evaluate the effect of the density of the surrounding material. Glass and resin microsphere simulations were performed with average and maximum diameter and density values (based on values given in the literature) in water and in liver. The results were compared to point source simulation results using the same transport code and in the same surrounding material. All simulations had statistical uncertainties less than 1%. RESULTS: The optimal transport settings in mcnp5 for this work included using the energy-and step-specific algorithm (DBCN 17J 2) and ESTEP set to 10. These settings were used for all subsequent simulations with mcnp5. The point source simulations in water for both egsnrc and mcnp5 were found to agree within 2% of the ICRU 72 reference data over the investigated range. Point source simulations in liver had large differences relative to ICRU 72, approaching -60% near the maximum range of (90)Y. These differences are mostly attributed to the difference in density between water (1.0 g[middle dot]cm(-3)) and liver (1.06 g[middle dot]cm(-3)). Glass and resin microsphere simulations showed a slight decrease in the dose rate near the maximum range of (90)Y relative to the point source simulations. The largest relative differences were approximately -4.2% and -2.8% for the glass and resin microspheres, respectively. Agreement between the egsnrc and mcnp5 simulations results was generally good. CONCLUSIONS: The presence of the microsphere material causes slight differences in the (90)Y dose kernel compared to those calculated with point sources. Large differences were seen between simulations in water and those in liver. For the most accurate calculation of the dose distribution, the density of the patient's liver should be accounted for in the calculation of the dose kernel. Lastly, due to the need to determine the optimal transport settings with mcnp5, electron transport with this code should be used with caution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".