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Record W3216038732 · doi:10.32920/ryerson.14652051.v1

Quantifying Radiobiological Variation in Cancer Radiotherapy Using Monte Carlo Simulation and Doped Plastic Scintillators

2021· preprint· en· W3216038732 on OpenAlexafffund
Humza Nusrat

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsToronto Metropolitan UniversityOntario Tech UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonte Carlo methodScintillatorContext (archaeology)Nuclear medicineBrachytherapyBeam (structure)PhysicsRadiation treatment planningRadiationPhotonOpticsComputational physicsMaterials scienceDetectorRadiation therapyMedicineStatisticsMathematicsRadiology

Abstract

fetched live from OpenAlex

This dissertation examines the extent to which radiobiological variations occur in photon radiotherapy, and then presents a novel methodology and detector prototype to measure this variation. In the first section, I examine the change in maximum RBE (RBEM) outside the primary field in open and composite 6 MV x-ray beams. This is done using Monte Carlo simulation and microdosimetric techniques. It was found that when comparing an open 10 10 cm2 6 MV beam to a composite 10 10 cm2 beam comprising one hundred 1x 1 cm2 beamlets, the out-of-field increase in RBE occurs much closer to the field edge in the composite case. This finding may have consequences for IMRT cases in which large amount of scattered radiation may be causing a higher than expected effective dose to organs at risk. In the second section, the maximum RBE variation is examined in the context of brachytherapy. The sources examined include 192Ir, 125I, and 169Yb. It was determined that maximum RBE of 125I relative to the source position did not vary significantly as distance from the source was increased, however, 192Ir and 169Yb were found to exhibit RBEM increases of 3.0% and 6.6% at a distance of 8 cm, respectively. Also, the impact of this variation on an HDR 192Ir prostate treatment plan was examined; it was found that RBEM hotspots of +3.6% occur at the treatment plan’s periphery. In the third part, the impact of lead doping on plastic scintillator response is quantified, a major step required for the development of the LET detector prototype. In this stage, 4 differently doped plastic scintillators were obtained, and measurements were conducted in low and medium LET beams. Using Geant4 Monte Carlo and the measured scintillator responses, the scintillator parameters: kB and L0 were determined as a function of dopant concentration and effective atomic number. Finally, the uniquely energy dependent scintillators were combined into a detector prototype used to measure the LET spectra produced by five low energy photon beams. These beams included four orthovoltage energies (100, 180, 250, and 300 kVp) along with an 192Ir HDR source. In this proof-of-principle work, the detector prototype and technique was found to accurately determine the LET spectra and the mean LET for all beams with the exception of the 100 kVp orthovoltage beam. Potential applications for the real-time LET detector prototype and technique described in this dissertation include LET measurement in radiotherapy, allowing for biologically optimized treatment plans improving patient care. This technique and prototype also has numerous applications in non-medical fields such as health physics, space travel dosimetry, and nuclear safety.

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 score0.006
Threshold uncertainty score0.011

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 science0.0010.000
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 head0.059
GPT teacher head0.373
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

Citations0
Published2021
Admission routes2
Has abstractyes

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