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By incorporating dynamic and static indicators, this study aimed to address this limitation by developing a machine learning (ML) model for HCC prediction in patients with CHB and compensated advanced chronic liver disease (cACLD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      ML models were trained to predict HCC in 1 272 patients with CHB and cACLD, who underwent at least 2 measurements from different centres and were further tested with external cohorts (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009601). The first and last liver stiffness measurements were defined as the baseline and follow\u2010up values, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 1 873 individuals with CHB and cACLD were included, with 192 patients (10.3%) developing HCC after a median of 30.7\u2009months. Based on LASSO logistic regression, baseline age, baseline albumin concentration, follow\u2010up albumin concentration, follow\u2010up platelet count, and change in the liver stiffness measurement (\u2206LSM) were selected, with \u2206LSM identified as the most important indicator. Developed using XGBoost, the ALPS\u2010HCC score achieved the highest integrated AUROC (0.852), Harrell's C\u2010index (0.852), and F1 score (0.944), and performed significantly better than existing HCC risk scores. Based on these results, our risk stratification performed well in terms of prognosis in the validation cohort, including the identification of low\u2010risk (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009250; reference), intermediate\u2010risk (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200991; hazard ratio [HR]\u2009=\u20095.25), and high\u2010risk (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200941; HR\u2009=\u200924.47) groups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ALPS\u2010HCC score provides robust predictive power and screening capability for predicting HCC risk in patients with CHB and cACLD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/liv.70866","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T06:53:03Z","timestamp":1789455183000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["<scp>ALPS<\/scp>\n                    \u2010\n                    <scp>HCC<\/scp>\n                    Score: A Dynamic Liver Stiffness Measurement\u2010Based Machine Learning Model to Predict Risk of Hepatocellular Carcinoma"],"prefix":"10.1111","volume":"46","author":[{"given":"Yinan","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Gastroenterology The Third Affiliated Hospital of Sun Yat\u2010Sen University  Guangzhou China"},{"name":"Guangdong Provincial Key Laboratory of Liver Disease Research  Guangzhou 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