{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T10:17:36Z","timestamp":1792059456838,"version":"4.4.0"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>Chronic hepatitis B (CHB) remains a major global cause of hepatocellular carcinoma (HCC), particularly in regions with high hepatitis B virus endemicity. Meanwhile, metabolic dysfunction-associated fatty liver disease (MAFLD) has emerged as a rapidly growing metabolic contributor to hepatocarcinogenesis. Their co-occurrence influences liver cancer development through complex biological interactions. This review aims to summarize recent advances regarding how CHB\u2013MAFLD comorbidity affects HCC risk and the development of related predictive models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This review synthesizes evidence from recent clinical and basic research exploring the relationship between CHB\u2013MAFLD comorbidity and cancer risk. It evaluates the performance of traditional and next-generation risk prediction models and examines the potential of artificial intelligence in integrating multimodal data to improve accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The underlying pathophysiology exhibits a dual nature. While metabolic disturbances can suppress hepatitis B virus replication and facilitate surface antigen clearance, metabolic dysfunctions such as obesity and diabetes independently accelerate fibrosis and increase cancer risk. This paradox originates from heterogeneous effects: simple steatosis may reduce risk, whereas cumulative metabolic abnormalities increase risk in a dose-dependent manner. Although traditional models retain prognostic value, their sensitivity is suboptimal. Next-generation models incorporating metabolic parameters have demonstrated improved accuracy. Furthermore, machine learning shows considerable potential for modeling complex non-linear interactions and enabling individualized risk prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This review summarizes how metabolic dysfunction-associated fatty liver disease influences cancer risk in patients with chronic hepatitis B. It provides a framework for identifying high-risk individuals to guide stratified, precision management strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1817642","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T05:48:58Z","timestamp":1781243338000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Metabolic dysfunction-associated fatty liver disease in chronic hepatitis B: dual effects on hepatocarcinogenesis and evolving strategies for risk prediction"],"prefix":"10.3389","volume":"17","author":[{"given":"Huihui","family":"Tao","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine, Naval Medical Centre, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, 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