Technical Note: The development of a multi‐physics simulation tool to estimate the background dose by systemic targeted alpha therapy
Bibliographic record
Abstract
PURPOSE: To predict biological effects of targeted alpha therapy (TAT) in preclinical studies, dosimetry calculations based on the micro-level distributions of emitters are essential. Due to the saturation of the tumor antigenic sites and bonding breaks by decay, some of Alpha-immuno-conjugate and decay daughters may inevitably be transported by convection and diffusion along with blood or lymphatic circulation. This results in highly nonuniform and unsteady distributions of irradiation sources. Since the micro-level distribution of emitters cannot be measured and obtained in patients with current technology, a modeling toolset to give more insight of the internal dose could be an alternative. METHODS: A multi-physics model based on a Monte Carlo microdosimetry technique and computational fluid dynamics (CFD) modeling was developed and applied to multiple internal irradiation sources. The CFD model tracks the path of the radionuclides and the dose model is capable of evaluating the time-dependent absorbed dose to the target. RESULTS: The conceptual model is capable of handling complex nonuniform irradiation sources in vasculature. The results from the simulations indicate that the assumption of homogeneous and motionless distribution of the administered activity used in the conventional dose calculation tends to significantly underestimate or overestimate the absorbed dose to the vascular system in various scenarios. CONCLUSION: Modeling the in vivo transport of radionuclides has the potential to improve the accuracy of TAT dose estimates. It could be the first step to develop a simulation tool set for assessing absorbed dose to tumor or normal tissues and predict the corresponding biological responses in the future.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".