Integration of Contrast-enhanced US into a Multimodality Approach to Imaging of Nodules in a Cirrhotic Liver: How I Do It
Bibliographic record
Abstract
Accurate characterization of cirrhotic nodules and early diagnosis of hepatocellular carcinoma (HCC) are of vital importance. Currently, computed tomography (CT) and magnetic resonance (MR) imaging are standard modalities for the investigation of new nodules found at surveillance ultrasonography (US). This article describes the successful integration of contrast material–enhanced US into a multimodality approach for diagnosis of HCC and its benefits in this population. The application of contrast-enhanced US immediately following surveillance US allows for prompt dynamic contrast-enhanced evaluation, removing the need for further imaging of benign lesions. Contrast-enhanced US also provides dynamic real-time assessment of tumor vascularity so that contrast enhancement can be identified regardless of its timing or duration, allowing for detection of arterial hypervascularity and portal venous washout. The purely intravascular nature of US contrast agents is valuable as the rapid washout of nonhepatocyte malignancies is highly contributory to their differentiation from HCC. The authors believe contrast-enhanced US provides complementary information to CT and MR imaging in the characterization of nodules in high-risk patients. © RSNA, 2017 Online supplemental material is available for this article.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".