WE‐D‐220‐04: Operator Uncertainties in a Soft Tissue 3D Ultrasound Image Guided Radiotherapy System
Bibliographic record
Abstract
Purpose: Image Guided Radiotherapy (IGRT) is a necessity for accurate radiotherapy. Ultrasound (US) imaging is a frequently used diagnostic technique for qualitative imaging of soft tissues. Recently, a quantitative 3D US system was introduced (Clarity system, Resonant Medical, Canada) which can assess the position of soft tissue in absolute space. Before introducing the device into daily practice, user variability, during both image acquisition and matching procedures must be determined. In this study we determined the inter‐ and intra‐operator variability of 3D US matching in prostate cancer patients. Moreover, we studied the influence of the scan variability on patient setup corrections, and the influence of probe pressure on the prostate position while using a strict bladder filling protocol. Methods: For 12 prostate patients multiple US scans are acquired by one or two operators during treatment. The repeated scans are matched to the reference US‐scan by a single user (variability for scanning). The remaining scans are matched three times by different users, and for each patient one single scan is matched five times by the same user (variability for matching). Results: In all three directions the mean intra‐operator difference ranges from 1.5 to 2.4 mm, with a standard deviation of approximately 1.7 mm. The mean inter‐operator difference is of the same order, 1.7 to 2.3 ± 1.8 mm. The prostate displacement due to the probe pressure varies from patient to patient and is not limited to one direction. Only the superior/inferior displacement seemed significant for high pressure, which was not needed to obtain good image quality with our bladder filling protocol. The total uncertainty is conservatively estimated to be 4 mm. Conclusions: The uncertainty of the 3D US IGRT system is comparable to the uncertainty of the current standard IGRT for prostate: electronic portal imager in combination with fiducial markers.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".