Variability and accuracy of measurements of prostate brachytherapy seed position <i>in vitro</i> using three‐dimensional ultrasound: An intra‐ and inter‐observer study
Bibliographic record
Abstract
This paper is a step in investigating whether three-dimensional (3D) ultrasound can be used intraoperatively to replace Computed Tomography (CT) for localization of brachytherapy seeds. In order to quantify the accuracy and variability of seed localization without introducing effects due to tissues, we first report our results with test phantoms. An inter- and intra-observer study was performed to assess the variability of 2 3D ultrasound scan acquisition methods: Tilt 3D scanning and pull-back 3D scanning. Seven observers measured the positions of gold seed markers in an agar phantom twice in each of the three orthogonal image planes. An analysis of variance (ANOVA) was performed to determine the intra- and inter-observer standard errors of measurement (SEM) and the minimum detectable changes in marker position (deltap). Average intra- and inter-observer SEMs for the tilt scan 3D image were 0.36 and 0.40 mm, respectively. Measurements of the pull-back scan 3D image yielded average intra- and inter-observer SEM of 0.46 and 0.49 mm, respectively. A paired difference analysis showed that the lower SEM for the tilt 3D scan image were statistically significant at a significance level of alpha= 0.05. The accuracy of the US measurements was tested by determining marker coordinates from CT images of the phantom in a stereotactic head frame. CT coordinates were matched to the ultrasound (US) coordinates by means of an affine transform. Average matching errors in x, y, and z were 0.02, 0.10, and -0.02 mm, respectively.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".