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Record W1989317200 · doi:10.1088/0031-9155/57/12/3885

Bone and mucosal dosimetry in skin radiation therapy: a Monte Carlo study using kilovoltage photon and megavoltage electron beams

2012· article· en· W1989317200 on OpenAlexaff
James C. L. Chow, Runqing Jiang

Bibliographic record

VenuePhysics in Medicine and Biology · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsGrand River HospitalUniversity of WaterlooUniversity of TorontoPrincess Margaret Cancer CentreToronto Metropolitan University
Fundersnot available
KeywordsImaging phantomMonte Carlo methodDosimetryPhotonLinear particle acceleratorElectronMaterials scienceIrradiationCathode rayRadiationNuclear medicineBeam (structure)PhysicsPercentage depth dose curveOpticsIonization chamberNuclear physicsMedicineMathematics

Abstract

fetched live from OpenAlex

This study examines variations of bone and mucosal doses with variable soft tissue and bone thicknesses, mimicking the oral or nasal cavity in skin radiation therapy. Monte Carlo simulations (EGSnrc-based codes) using the clinical kilovoltage (kVp) photon and megavoltage (MeV) electron beams, and the pencil-beam algorithm (Pinnacle(3) treatment planning system) using the MeV electron beams were performed in dose calculations. Phase-space files for the 105 and 220 kVp beams (Gulmay D3225 x-ray machine), and the 4 and 6 MeV electron beams (Varian 21 EX linear accelerator) with a field size of 5 cm diameter were generated using the BEAMnrc code, and verified using measurements. Inhomogeneous phantoms containing uniform water, bone and air layers were irradiated by the kVp photon and MeV electron beams. Relative depth, bone and mucosal doses were calculated for the uniform water and bone layers which were varied in thickness in the ranges of 0.5-2 cm and 0.2-1 cm. A uniform water layer of bolus with thickness equal to the depth of maximum dose (d(max)) of the electron beams (0.7 cm for 4 MeV and 1.5 cm for 6 MeV) was added on top of the phantom to ensure that the maximum dose was at the phantom surface. From our Monte Carlo results, the 4 and 6 MeV electron beams were found to produce insignificant bone and mucosal dose (<1%), when the uniform water layer at the phantom surface was thicker than 1.5 cm. When considering the 0.5 cm thin uniform water and bone layers, the 4 MeV electron beam deposited less bone and mucosal dose than the 6 MeV beam. Moreover, it was found that the 105 kVp beam produced more than twice the dose to bone than the 220 kVp beam when the uniform water thickness at the phantom surface was small (0.5 cm). However, the difference in bone dose enhancement between the 105 and 220 kVp beams became smaller when the thicknesses of the uniform water and bone layers in the phantom increased. Dose in the second bone layer interfacing with air was found to be higher for the 220 kVp beam than that of the 105 kVp beam, when the bone thickness was 1 cm. In this study, dose deviations of bone and mucosal layers of 18% and 17% were found between our results from Monte Carlo simulation and the pencil-beam algorithm, which overestimated the doses. Relative depth, bone and mucosal doses were studied by varying the beam nature, beam energy and thicknesses of the bone and uniform water using an inhomogeneous phantom to model the oral or nasal cavity. While the dose distribution in the pharynx region is unavailable due to the lack of a commercial treatment planning system commissioned for kVp beam planning in skin radiation therapy, our study provided an essential insight into the radiation staff to justify and estimate bone and mucosal dose.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.377
Teacher spread0.314 · 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

Citations16
Published2012
Admission routes1
Has abstractyes

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