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Record W2069763292 · doi:10.1088/0031-9155/47/14/305

Tumour dose enhancement using modified megavoltage photon beams and contrast media

2002· article· en· W2069763292 on OpenAlexaff
James L. Robar, Silvia A. Riccio, Montgomery Martin

Bibliographic record

VenuePhysics in Medicine and Biology · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsImaging phantomMonte Carlo methodMaterials scienceNuclear medicineLinear particle acceleratorGadoliniumPhotonOpticsBeam (structure)Ionization chamberScannerIonizationPhysicsMedicineMathematicsIon

Abstract

fetched live from OpenAlex

This study examines the magnitude of tumour dose enhancement achieved by injection of gadolinium or iodine contrast media (CM) and treatment using modified x-ray photon spectra from linear accelerators. Monte Carlo modelling of the linear accelerator and patient geometry was used to explore the effect of removing the flattening filter for various beam qualities and the resultant effect on dose enhancement. In addition, ionization measurements were conducted to observe dose enhancement within a phantom containing CM. Simulation results indicate that for flattened 6-24 MV photon beams and realistic CM tumour concentrations, the dose enhancement remains below 5%. However, if the flattening filter is removed, dose enhancement is increased significantly. For a 30 mg ml(-1) gadolinium CM tumour concentration, for example, 8.4%, 10.8%, 13.7% and 23.1% dose enhancements are achieved for 18 MV, 6 MV, 4 MV and 2 MV unflattened beams, respectively. In contrast to the phototherapy technique, which uses the orthovoltage beam from a modified CT scanner to achieve dose enhancement, all unflattened spectra preserve the dose build-up at the surface, and thus the skin and bone are spared.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.123
GPT teacher head0.364
Teacher spread0.241 · 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

Citations131
Published2002
Admission routes1
Has abstractyes

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