MétaCan
Menu
Back to cohort
Record W3092813170 · doi:10.3390/app10207052

Depth Dose Enhancement on Flattening-Filter-Free Photon Beam: A Monte Carlo Study in Nanoparticle-Enhanced Radiotherapy

2020· article· en· W3092813170 on OpenAlexaff
James C. L. Chow

Bibliographic record

VenueApplied Sciences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMonte Carlo methodMaterials sciencePhotonNanoparticlePhoton energyRange (aeronautics)AttenuationBeam (structure)FlatteningOpticsAnalytical Chemistry (journal)ChemistryPhysicsNanotechnologyComposite materialMathematics

Abstract

fetched live from OpenAlex

The aim of this study is to investigate the variations of depth dose enhancement (DDE) on different nanoparticle (NP) variables, when using the flattening-filter-free (FFF) photon beam in nanoparticle-enhanced radiotherapy. Monte Carlo simulation under a macroscopic approach was used to determine the DDE ratio (DDER) with variables of NP material (gold (Au) and iron (III) oxide (Fe2O3)), NP concentration (3–40 mg/mL) and photon beam (10 MV flattening-filter (FF) and 10 MV FFF). It is found that Au NPs had a higher DDER than Fe2O3 NPs, when the depths were shallower than 6 and 8 cm for the 10 MV FF and 10 MV FFF photon beams, respectively. However, in a deeper depth range of 10–20 cm, DDER for the Au NPs was lower than Fe2O3 NPs mainly due to the beam attenuation and photon energy distribution. It is concluded that DDER for the Au NPs and Fe2O3 NPs decreased with an increase of depth in the range of 10–20 cm, with rate of decrease depending on the NP material, NP concentration and the use of FF in the photon beam.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.293
Teacher spread0.270 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations22
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueApplied SciencesSame topicAdvanced Radiotherapy TechniquesFrench-language works237,207