Successful Integration of Contrast-enhanced US into Routine Abdominal Imaging
Bibliographic record
Abstract
Contrast material–enhanced US is recognized increasingly as a useful tool in a wide variety of hepatic and nonhepatic applications. The modality recently was approved for limited use for liver indications in adult and pediatric patients in the United States. Contrast-enhanced US uses microbubbles of gas injected intravenously as a contrast agent to demonstrate blood flow and tissue perfusion. The growing worldwide application of contrast-enhanced US in multiple organ systems is due largely to its advantages, including high contrast resolution (sensitivity to the contrast agent), real-time imaging, lack of nephrotoxicity, the purely intravascular property of microbubble contrast agents that allows the use of disruption-replenishment techniques, and repeatability during the same examination. Through illustrative cases, common useful clinical scenarios are discussed, including characterization of liver and renal masses, especially indeterminate lesions at CT or MRI; differentiation of neoplastic cysts from nonneoplastic cysts in various organs; differentiation of tumor thrombus from bland thrombus; and assessment after a renal transplant or local ablative therapy. Common applications in the biliary system, pancreas, spleen, and vasculature also are introduced. Successful routine use of contrast-enhanced US requires an efficient setup and workflow and a thorough understanding of appropriate clinical indications and its advantages that provide added value after CT and MRI. This article familiarizes radiologists with common abdominal applications of contrast-enhanced US and guides them to implement contrast-enhanced US successfully in their clinical practice. Online supplemental material is available for this article. ©RSNA, 2018
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| 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".