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Record W2579898834 · doi:10.1088/1361-6560/aa5136

A Monte Carlo study of macroscopic and microscopic dose descriptors for kilovoltage cellular dosimetry

2017· article· en· W2579898834 on OpenAlexafffund
Patricia Oliver, Rowan M. Thomson

Bibliographic record

VenuePhysics in Medicine and Biology · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsCytoplasmNucleusMonte Carlo methodPhotonDosimetryRadiobiologyPhysicsBiophysicsMaterials scienceChemistryNuclear medicineIrradiationBiologyNuclear physicsMedicineOpticsCell biology

Abstract

fetched live from OpenAlex

This work investigates how doses to cellular targets depend on cell morphology, as well as relations between cellular doses and doses to bulk tissues and water. Multicellular models of five healthy and cancerous soft tissues are developed based on typical values of cell compartment sizes, elemental compositions and number densities found in the literature. Cells are modelled as two concentric spheres with nucleus and cytoplasm compartments. Monte Carlo simulations are used to calculate the absorbed dose to the nucleus and cytoplasm for incident photon energies of 20-370 keV, relevant for brachytherapy, diagnostic radiology, and out-of-field radiation in higher-energy external beam radiotherapy. Simulations involving cell clusters, single cells and single nuclear cavities are carried out for cell radii between 5 and [Formula: see text]m, and nuclear radii between 2 and [Formula: see text]m. Seven nucleus and cytoplasm elemental compositions representative of animal cells are considered. The presence of a cytoplasm, extracellular matrix and surrounding cells can affect the nuclear dose by up to [Formula: see text]. Differences in cell and nucleus size can affect dose to the nucleus (cytoplasm) of the central cell in a cluster of 13 cells by up to [Formula: see text] ([Formula: see text]). Furthermore, the results of this study demonstrate that neither water nor bulk tissue are reliable substitutes for subcellular targets for incident photon energies <50 keV: nuclear (cytoplasm) doses differ from dose-to-medium by up to [Formula: see text] ([Formula: see text]), and from dose-to-water by up to [Formula: see text] ([Formula: see text]). The largest differences between dose descriptors are seen for the lowest incident photon energies; differences are less than [Formula: see text] for energies [Formula: see text]90 keV. The sensitivity of results with regard to the parameters of the microscopic tissue structure model and cell model geometry, and the importance of the nucleus and cytoplasm as targets for radiation-induced cell death emphasize the importance of accurate models for cellular dosimetry studies.

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)0.001
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.122
GPT teacher head0.415
Teacher spread0.293 · 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

Citations21
Published2017
Admission routes2
Has abstractyes

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