Modelling the use of solid state detectors to determine seed locations in low dose rate brachytherapy
Bibliographic record
Abstract
Brachytherapy involves the treatment of cancer through the implantation of radioactive seeds into the tumour. A high dose of radiation is given to the tumour while healthy tissue surrounding the tumour receives only a small dose. By implanting multiple seeds into the tumour at selected locations, the necessary dose may be given to the tumour covering the entire geometry of the tumour. If the placement of the seeds within the tumour is inaccurate, the dose distribution within the tumour will be affected. The tumour may receive a smaller than required dose, or the healthy tissue surrounding the tumour may receive an excessive dose. The positioning of the seeds within the tumour needs to be monitored in real time during insertion in order to make corrections for misplaced seeds. The following thesis presents a detailed account of experiments conducted at the Centre for Medical Radiation Physics, University of Wollongong. The goal of these experiments was to conduct Monte Carlo simulations of brachytherapy seeds in water to study how certain characteristics of the radiation distribution, arising from these seeds, vary at different positions in water around the seeds. An outcome of this was to perceive a need to develop a system to determine the location of brachytherapy seeds within the body using measured characteristics of the radiation distribution as determined using solid state detectors. The code used for the Monte Carlo simulations was Egsnrc V2 (Electron Gamma Shower 4, modified by the National Research Council of Canada). The user subroutines used were Dosrznrc, to estimate the dose distribution around the seeds, and Flurznrc, to estimate the radiation spectrum distribution around the seeds. The seeds studied were OncoSeed number 6711 from
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".