Contrast-enhanced Ultrasound—State of the Art in North America
Bibliographic record
Abstract
On October 24, 2017, in Chicago, the Society of Radiologists in Ultrasound convened a panel of specialists in contrast-enhanced ultrasound (CEUS) to arrive at a white paper regarding the use of CEUS in noncardiac applications in North America. Recommendations are based on analysis of the current literature and common practice strategies. They represent a reasonable approach to introduce the advantages of this safe and noninvasive technique for the benefit of our patients. Characterization of liver nodules with CEUS, as the approval indication worldwide, is the major focus of this endeavor. In addition, many off label uses are reviewed and literature supporting these indications provided.Key Points(1) Contrast-enhanced ultrasound (CEUS) allows cross-sectional imaging of the liver, kidneys and multiple other solid and hollow viscera, providing excellent characterization of identified focal mass lesions.(2) Performed with the injection of a microbubble contrast agent, CEUS provides a safe and readily available imaging technique which requires no ionizing radiation, making it appropriate for use in all ages, in those with renal insufficiency and when a portable examination is needed.(3) The CEUS can be considered in abdominal imaging whenever blood flow information is of value to diagnosis.(4) Dynamic real-time acquisition and the use of a purely intravascular contrast agent are the 2 most essential technical aspects of CEUS imaging which distinguish it from both computed tomography and magnetic resonance scan.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".