Hepatocellular Carcinoma in Evolution: Correlation with CEUS LI-RADS
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver with a high incidence worldwide and a high associated mortality. Well-recognized risk factors that cause a predisposition to the development of HCC include chronic infection with the hepatitis B or C virus, alcohol-related and non–alcohol-related fatty liver disease, and cirrhosis. In these chronically diseased livers, benign regenerative nodules can increase in size and develop cellular atypia that progress into dysplastic nodules and ultimately HCC. This sequence of hepatocarcinogenesis is coupled with changes in nodule vascularity, including progressive decreased density of portal triads and induced neoangiogenesis, resulting in increased hepatic arterial recruitment. Changes in vascularity result in an array of patterns of nodule enhancement and washout, which can be sensitively depicted with dynamic real-time contrast-enhanced US. Regenerative nodules are isoenhancing relative to the liver with all phases, while HCC classically shows avid arterial phase hyperenhancement with late mild washout. In between, there is great variation as nodules evolve through progressive grades of dysplasia toward HCC. Observed patterns of enhancement and washout can be used to diagnose or stratify the risk of malignancy in liver nodules by using the diagnostic algorithm described by the American College of Radiology Liver Imaging Reporting and Data System (LI-RADS). This facilitates the detection and close monitoring of potential early-stage disease. LI-RADS categorizes nodules according to a probabilistic likelihood for HCC with criteria for LR-5 nodules that are highly specific for the diagnosis of HCC, allowing treatment without exposing the patient to invasive biopsy. An invited commentary by Fetzer is available online. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. ©RSNA, 2022
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".