Contrast-enhanced US Approach to the Diagnosis of Focal Liver Masses
Bibliographic record
Abstract
Focal liver lesions are commonly encountered and often demonstrate nonspecific findings at initial imaging. Although most incidentally discovered liver lesions are benign, their noninvasive diagnosis is necessary, especially if they are large or atypical. Imaging characterization of focal liver lesions and exclusion of malignancy are of prime importance, particularly in high-risk populations. Contrast agent–enhanced ultrasonography of liver lesions is both accurate and reproducible for evaluation of benign and malignant liver tumors. Use of an imaging algorithm and a controlled sonographic technique, including dedicated arterial phase cine imaging and imaging every 30 seconds in the portal venous phase and the delayed (or late) phase, is essential for accurate characterization. This algorithmic analysis of focal liver lesions focuses first on the determination of malignancy by imaging the portal venous phase and the late phase; washout in these phases correlates with a malignant tumor, and sustained enhancement in these phases is suggestive that a lesion is benign. In addition, the timing and the intensity of washout differentiate hepatocellular malignancies from nonhepatocellular malignancies. Nonhepatocellular tumors demonstrate early and strong washout, whereas hepatocellular malignancies show delayed and weak washout. Subsequent analysis of dynamic real-time enhancement patterns in the arterial phase demonstrates specific enhancement patterns of common benign and malignant focal liver lesions. Hemangiomas show classic peripheral nodular enhancement, and spoke-wheel centrifugal enhancement is suggestive of focal nodular hyperplasia. Hepatic adenomas may show centripetal filling. However, arterial phase enhancement in malignancy has less specificity. Online supplemental material is available for this article. ©RSNA, 2017 •
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".