Monte Carlo simulation of radiation transport and dose deposition from locally released gold nanoparticles labeled with<sup>111</sup>In,<sup>177</sup>Lu or<sup>90</sup>Y incorporated into tissue implantable depots
Bibliographic record
Abstract
Abstract Permanent seed implantation (PSI) brachytherapy is a highly conformal form of radiation therapy but is challenged with dose inhomogeneity due to its utilization of low energy radiation sources. Gold nanoparticles (AuNP) conjugated with electron emitting radionuclides have recently been developed as a novel form of brachytherapy and can aid in homogenizing dose through physical distribution of radiolabeled AuNP when injected intratumorally (IT) in suspension. However, the distribution is unpredictable and precise placement of many injections would be difficult. Previously, we reported the design of a nanoparticle depot (NPD) that can be implanted using PSI techniques and which facilitates controlled release of AuNP. We report here the 3D dose distribution resulting from a NPD incorporating AuNP labeled with electron emitters ( 90 Y, 177 Lu, 111 In) of different energies using Monte Carlo based voxel level dosimetry. The MCNP5 Monte Carlo radiation transport code was used to assess differences in dose distribution from simulated NPD and conventional brachytherapy sources, positioned in breast tissue simulating material. We further compare these dose distributions in mice bearing subcutaneous human breast cancer xenografts implanted with 177 Lu-AuNP NPD, or injected IT with 177 Lu-AuNP in suspension. The radioactivity distributions were derived from registered SPECT/CT images and time-dependent dose was estimated. Results demonstrated that the dose distribution from NPD reduced the maximum dose 3-fold when compared to conventional seeds. For simulated NPD, as well as NPD implanted in vivo , 90 Y delivered the most homogeneous dose distribution. The tumor radioactivity in mice IT injected with 177 Lu-AuNP redistributed while radioactivity in the NPD remained confined to the implant site. The dose distribution from radiolabeled AuNP NPD were predictable and concentric in contrast to IT injected radiolabeled AuNP, which provided irregular and temporally variant dose distributions. The use of NPD may serve as an intermediate between PSI and radiation delivered by radiolabeled AuNP by providing a controlled method to improve delivery of prescribed doses as well as homogenize dose from low penetrating electron sources.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".