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Record W2603427157 · doi:10.1088/1361-6560/aa6a42

Cavity theory applications for kilovoltage cellular dosimetry

2017· article· en· W2603427157 on OpenAlexaff
Patricia Oliver, Rowan M. Thomson

Bibliographic record

VenuePhysics in Medicine and Biology · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsPhotonDosimetryMonte Carlo methodPhysicsRange (aeronautics)Computational physicsEnergy (signal processing)Statistical physicsAtomic physicsOpticsMathematicsMaterials scienceStatisticsNuclear medicineQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Relationships between macroscopic (bulk tissue) and microscopic (cellular) dose descriptors are investigated using cavity theory and Monte Carlo (MC) simulations. Small, large, and multiple intermediate cavity theory (SCT, LCT, and ICT, respectively) approaches are considered for 20 to 370 keV incident photons; ICT is a sum of SCT and LCT contributions weighted by parameter d . Considering μ m-sized cavities of water in bulk tissue phantoms, different cavity theory approaches are evaluated via comparison of D w , m / D m , m (where D w , m is dose-to-water-in-medium and D m , m is dose-to-medium-in-medium) with MC results. The best overall agreement is achieved with an ICT approach in which d = ( 1 − e − β L ) / ( β L ) , where L is the mean chord length of the cavity and β is given by e − β R C S D A = 0.04 ( R CSDA is the continuous slowing down approximation range of an electron of energy equal to that of incident photons). Cell nucleus doses, D nuc , computed with this ICT approach are compared with those from MC simulations involving multicellular soft tissue models considering a representative range of cell/nucleus sizes and elemental compositions. In 91 % of cases, ICT and MC predictions agree within 3 % ; disagreement is at most 8.8%. These results suggest that cavity theory may be useful for linking doses from model-based dose calculation algorithms (MBDCAs) with energy deposition in cellular targets. Finally, based on the suggestion that clusters of water molecules associated with DNA are important radiobiological targets, two approaches for estimating dose-to-water by application of SCT to MC results for D m , m or D nuc are compared. Results for these two estimates differ by up to 35 % , demonstrating the sensitivity of energy deposition within a small volume of water in nucleus to the geometry and composition of its surroundings. In terms of the debate over the dose specification medium for MBDCAs, these results do not support conversion of D m , m to D w , m using SCT.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.421
Teacher spread0.316 · 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 designTheoretical or conceptual
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

Citations6
Published2017
Admission routes1
Has abstractyes

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