Real-Time Ultrasound-Computed Tomography Fusion with Volume Navigation to Assess Pancreatic Cystic Lesions
Bibliographic record
Abstract
Transabdominal ultrasound is a promising imaging modality for pancreatic cystic lesions. This study aims to determine if transabdominal ultrasonography with CT fusion (TAUS-f) using volume navigation can be used to measure pancreatic cystic lesions (PCLs) compared to CT alone. We evaluated 33 patients prospectively with known PCLs. The readers evaluated each PCL's size and imaging characteristics on TAUS-f and CT alone. These were compared to endoscopic ultrasonography reports. A total of 43 PCLs from 32 patients were evaluated. The detection rate by TAUS-f was 93%. Two of the three undetected PCLs were in the tail of the pancreas. Inter-reader variabilities for TAUS-f and CT were 0.005 cm and 0.03 cm, respectively. Subgroup analysis by size and location demonstrated that inter-modality variability between TAUS-f and CT was smallest for lesions < 1.5 cm with a size difference of -0.13 cm for each reader and smallest in the pancreatic head with a size difference of -0.16 cm and -0.17 cm for readers 1 and 2. We found that TAUS-f effectively evaluates PCLs compared to CT alone, thus suggesting that it should be considered part of the surveillance algorithm for a subset of patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".