Imaging Findings in Cirrhotic Liver: Pearls and Pitfalls for Diagnosis of Focal Benign and Malignant Lesions
Bibliographic record
Abstract
Cirrhosis is the end stage of chronic liver disease and causes architectural distortion and perfusional anomalies. It is a major risk factor for developing hepatocellular carcinoma (HCC). Common disease entities in noncirrhotic livers, such as hemangiomas, can be rare in cirrhotic livers, and benign entities such as confluent hepatic fibrosis and focal nodular hyperplasia–like lesions may mimic the appearance of malignancies,. HCC usually has typical imaging characteristics, such as the major features established by the Liver Imaging Reporting and Data System. However, HCC can also have a spectrum of atypical or uncommon appearances, such as cystic HCC, hypovascular HCC, or macroscopic fat–containing HCC. HCCs with certain genetic mutations such as CTNNB-1–mutated HCC can harbor unique imaging features not seen in other types of HCC. In addition, malignancies that are less common than HCC, such as cholangiocarcinoma and metastases, which can be difficult to differentiate, can still occur in cirrhotic livers. Atypical imaging features of benign and malignant lesions can be challenging to accurately diagnose. Therefore, familiarity with these features and an understanding of the prevalence of disease entities in cirrhotic livers are key in the daily practice of radiologists for evaluation of cirrhotic livers. The authors illustrate the typical and atypical features of benign and malignant lesions in cirrhosis and discuss the technical pitfalls and unique advantages associated with various imaging modalities in assessing cirrhotic livers, including noncontrast and contrast-enhanced US, CT, and MRI.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".