Patient centered HCC surveillance - complementary roles of ultrasound and CT/MRI
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide and is the fastest growing cause of cancer death in the United States (U.S.) In the U.S., current national clinical practice guidelines from the 2023 American Association for the Study of Liver Diseases (AASLD) Practice Guidance and the recently updated Liver Imaging Reporting & Data Systems (LI-RADS) Ultrasound (US) Surveillance v2024 core recommend semi-annual serum α-fetoprotein and US screening of patients deemed to be high risk for developing HCC. In this article, we will explore the transition to a patient-centered approach to HCC surveillance, including the role of the new LI-RADS US Surveillance v2024 core and the use of visualization score for determining ultrasound quality, the known risk factors for poor US image quality, and the potential options for alternative surveillance strategies when US may not be a viable option for certain patients, including multiphasic computed tomography (CT), magnetic resonance imaging (MRI), and several abbreviated MRI protocols.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".