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Record W4377220858 · doi:10.1002/mp.16454

Multiscale Monte Carlo simulations of gold nanoparticle dose‐enhanced radiotherapy I: Cellular dose enhancement in microscopic models

2023· article· en· W4377220858 on OpenAlexafffund
Martin P. Martinov, Elizabeth M. Fletcher, Rowan M. Thomson

Bibliographic record

VenueMedical Physics · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonte Carlo methodColloidal goldPhotonMaterials sciencePhysicsComputational physicsNanoparticleMolecular physicsNanotechnologyOpticsMathematicsStatistics

Abstract

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Abstract Background The introduction of Gold NanoParticles (GNPs) in radiotherapy treatments necessitates considerations such as GNP size, location, and quantity, as well as patient geometry and beam quality. Physics considerations span length scales across many orders of magnitude (nanometer‐to‐centimeter), presenting challenges that often limit the scope of dosimetric studies to either micro‐ or macroscopic scales. Purpose To investigate GNP dose‐enhanced radiation Therapy (GNPT) through Monte Carlo (MC) simulations that bridge micro‐to‐macroscopic scales. The work is presented in two parts, with Part I (this work) investigating accurate and efficient MC modeling at the single cell level to calculate nucleus and cytoplasm Dose Enhancement Factors (n,cDEFs), considering a broad parameter space including GNP concentration, GNP intracellular distribution, cell size, and incident photon energy. Part II then evaluates cell dose enhancement factors across macroscopic (tumor) length scales. Methods Different methods of modeling gold within cells are compared, from a contiguous volume of either pure gold or gold‐tissue mixture to discrete GNPs in a hexagonal close‐packed lattice. MC simulations with EGSnrc are performed to calculate n,cDEF for a cell with radius µm and nucleus µm considering 10 to 370 keV incident photons, gold concentrations from 4 to 24 mg Au /g tissue , and three different GNP configurations within the cell: GNPs distributed around the surface of the nucleus (perinuclear) or GNPs packed into one (or four) endosome(s). Select simulations are extended to cells with different cell (and nucleus) sizes: 5 µm (2, 3, and 4 µm), 7.35 µm (4 and 6 µm), and 10 µm (7, 8, and 9 µm). Results n,cDEFs are sensitive to the method of modeling gold in the cell, with differences of up to 17% observed; the hexagonal lattice of GNPs is chosen (as the most realistic model) for all subsequent simulations. Across cell/nucleus radii, source energies, and gold concentrations, both nDEF and cDEF are highest for GNPs in the perinuclear configuration, compared with GNPs in one (or four) endosome(s). Across all simulations of the ( r cell , r nuc ) = (7.35, 5) µm cell, nDEFs and cDEFs range from unity to 6.83 and 3.87, respectively. Including different cell sizes, nDEFs and cDEFs as high as 21.5 and 5.5, respectively, are observed. Both nDEF and cDEF are maximized at photon energies above the K‐ or L‐edges of gold by 10 to 20 keV. Conclusions Considering 5000 unique simulation scenarios, this work comprehensively investigates many physics trends on DEFs at the cellular level, including demonstrating that cellular DEFs are sensitive to gold modeling approach, intracellular GNP configuration, cell/nucleus size, gold concentration, and incident source energy. These data should prove especially useful in research as well as treatment planning, allowing one to optimize or estimate DEF using not only GNP uptake, but also account for average tumor cell size, incident photon energy, and intracellular configuration of GNPs. Part II will expand the investigation, taking the Part I cell model and applying it in cm‐scale phantoms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.306
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2023
Admission routes2
Has abstractyes

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