Contrast-enhanced ultrasound approach to the diagnosis of focal liver lesions: the importance of washout
Bibliographic record
Abstract
Contrast-enhanced ultrasound (CEUS) is a powerful technique for differentiating focal liver lesions (FLLs) without the risks of potential nephrotoxicity or ionizing radiation. In the diagnostic algorithm for FLLs on CEUS, washout is an important feature, as its presence is highly suggestive of malignancy and its characteristics are useful in distinguishing hepatocellular from nonhepatocellular malignancies. Interpreting washout on CEUS requires an understanding that microbubble contrast agents are strictly intravascular, unlike computed tomography or magnetic resonance imaging contrast agents. This review explains the definition and types of washout on CEUS in accordance with the 2017 version of the CEUS Liver Imaging Reporting and Data System and presents their applications to differential diagnosis with illustrative examples. Additionally, we propose potential mechanisms of rapid washout and describe the washout phenomenon in benign entities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".