← Issue №14/ week of Oct 4, 2026/Hepatology

ALPS-HCC Score: A Dynamic Liver Stiffness Measurement-Based Machine Learning Model to Predict Risk of Hepatocellular Carcinoma.

From GI Signals issue №14: what this paper found, what it changes, and where it sits against the current standard of care, reviewed by Simon Mathews, MD.

Hepatology prospective cohort · n=1,873 · Oct 1, 2026 · Liver International · IF 6.7

ALPS-HCC Score: A Dynamic Liver Stiffness Measurement-Based Machine Learning Model to Predict Risk of Hepatocellular Carcinoma.

New evidencehepatocellular carcinomaartificial intelligencebiomarkerepidemiology
Clinical takeawayConsider using the ALPS-HCC score to stratify HCC risk in patients with CHB and compensated advanced chronic liver disease (cACLD), incorporating baseline age, albumin levels, platelet count, and change in liver stiffness measurement over time to guide surveillance strategies.
What it foundThe ALPS-HCC score stratified HCC risk in CHB patients with cACLD into low-risk (reference), intermediate-risk (HR=5.25), and high-risk (HR=24.47) groups, with an AUROC of 0.852, outperforming existing HCC risk scores reliant on static indicators.
ContextThis refines current HCC risk prediction by integrating dynamic liver stiffness changes and other biomarkers, demonstrating robust predictive power in both training and validation cohorts.
No standard claimed for this paper

Every paper is compared to the standard governing its question. This one is not: either no standard in the corpus matches it, or the comparison did not hold up on review and was withdrawn rather than published unverified. Both are logged.

Huang Y … Yang Y · Liver International : Official Journal of the International Association for the Study of the Liver · IF 6.7 · PubMed ↗Permalink
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