HetMS-MC: A framework for heterogeneous multiscale Monte Carlo modelling in radiation medicine
Bibliographic record
Abstract
BACKGROUND: Radiation medicine involves processes spanning many length scales and there is interest in modelling across scales to advance knowledge, particularly for prospective treatment approaches, e.g., gold nanoparticle-enhanced radiation therapy (GNPT). Previously, implementation of this type of multiscale modelling in radiation medicine has been problem-based, with little standardization and no established framework. PURPOSE: To introduce a framework for heterogeneous multiscale Monte Carlo (HetMS-MC) modelling in radiation medicine. METHODS: The presented framework includes considerations for the creation and analysis of HetMS-MC models, including model development, simulation setup, uncertainty analysis and quantification. The framework is demonstrated through two examples: (1) tumour model; (2) GNPT scenario. Both are implemented in EGSnrc and consist of a cm-scale tumour containing a region of interest comprised of 650 micron-scale cells in which specific energy is scored. RESULTS: The tumour model demonstrates the need for multiscale modelling as the HetMS-MC model captures differences in specific energy distributions from varying input parameters such as cell arrangement and MC random number seed that are not seen in conventional MC simulations. The GNPT model highlights the importance of multi-scale bridging in the development of HetMS-MC models. CONCLUSIONS: A general HetMS-MC framework is established and used to develop two models relevant to radiation medicine. This framework allows for efficient simulation of radiation physics processes across many length scales for arbitrary treatment modalities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".