Improved Dosimetry in Radiotherapy with Gold-Coated Fiber Optics
Bibliographic record
Abstract
Background: Patients receiving radiotherapy, a clinical treatment process in which radiation is used for the treatment of various types of cancer, utilize a variety of radiation sources with unique characteristics and procedures.In vivo dose measurements can help identify systematic and random errors in delivery of the treatment and therefore play an important role in quality assurance.Recently, the photon response of optical fibers has been investigated by many research groups.The small diameters of optical fibers increase the possibility of producing a dosimeter with high spatial resolution, important in the sense that an accurate value for the absorbed dose in the surrounding tissue of the dosimeter can be more accurately reported.Another important advantage of optical fibers as radiation dosimeters is that, unlike conventional TLDs, optical fibers are impervious to water.The aim of this study is to verify the dosimetric use of fiber optics in interface dosimetry and to investigate a novel enhanced dose technique using different gold thicknesses as a coating for fiber optics.Methods: To achieve this goal, commercially available Ge-doped SiO2 optical fibers (Cor Active, Canada) with a core diameter of 50.9 ± 4.1 µm were irradiated using a 250 kVp superficial X-ray machine and a dose of 3 Gy.Before irradiation, fiber optics were prepared and the following steps, preheat annealing and reading, were performed. Results:The results show enhancement with increasing gold (Au) thickness, with the highest percentage dose enhancement of approximately 160% obtained at 80 nm.A slight deviation from the enhancement was obtained at 20 nm, the first thickness of gold.Encouraging results from such studies have paved the way for the development of optical fiber radiation dosimeters specifically tailored to the task of dosimetry in radiotherapy.Conclusion: An optical fiber dosimeter can be placed within the tissue of interest, which is applicable due to its flexibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".