Source strength verification and quality assurance of preloaded brachytherapy needles using a CMOS flat panel detector
Bibliographic record
Abstract
PURPOSE: Current methods of low dose rate brachytherapy source strength verification for sources preloaded into needles consist of either assaying a small number of seeds from a separate sample belonging to the same lot used to load the needles or performing batch assays of a subset of the preloaded seed trains. Both of these methods are cumbersome and have the limitations inherent to sampling. The purpose of this work was to investigate an alternative approach that uses an image-based, autoradiographic system capable of the rapid and complete assay of all sources without compromising sterility. METHODS: The system consists of a flat panel image detector, an autoclavable needle holder, and software to analyze the detected signals. The needle holder was designed to maintain a fixed vertical spacing between the needles and the image detector, and to collimate the emissions from each seed. It also provides a sterile barrier between the needles and the imager. The image detector has a sufficiently large image capture area to allow several needles to be analyzed simultaneously.Several tests were performed to assess the accuracy and reproducibility of source strengths obtained using this system. Three different seed models (Oncura 6711 and 9011 (125)I seeds, and IsoAid Advantage (103)Pd seeds) were used in the evaluations. Seeds were loaded into trains with at least 1 cm spacing. RESULTS: Using our system, it was possible to obtain linear calibration curves with coverage factor k = 1 prediction intervals of less than ±2% near the centre of their range for the three source models. The uncertainty budget calculated from a combination of type A and type B estimates of potential sources of error was somewhat larger, yielding (k = 1) combined uncertainties for individual seed readings of 6.2% for (125)I 6711 seeds, 4.7% for (125)I 9011 seeds, and 11.0% for Advantage (103)Pd seeds. CONCLUSIONS: This study showed that a flat panel detector dosimetry system is a viable option for source strength verification in preloaded needles, as it is capable of measuring all of the sources intended for implantation. Such a system has the potential to directly and efficiently estimate individual source strengths, the overall mean source strength, and the positions within the seed-spacer train.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".