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Advances in Hepatology (2nd Edition)

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Editor

Dr. Yuanwen Chen

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Guest Editor

Department of Gastroenterology, Huadong Hospital, Fudan University, Shanghai 200040, China
Interests: metabolic dysfunction-associated steatotic liver disease (MASLD); metabolic dysfunction-associated steatohepatitis (MASH); alcohol-associated liver disease; alcohol-associated hepatitis; liver fibrosis; gut microbiota; macrophages; neutrophils
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Special Issue Information

Dear Colleagues,

This Special Issue, “Advances in Hepatology (2nd Edition)”, aims to highlight cutting-edge research and emerging concepts across the spectrum of liver diseases. We welcome original research and comprehensive reviews on metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), alcoholic liver disease (ALD), alcoholic hepatitis, and related complications such as fibrosis, cirrhosis, and portal hypertension. In addition, this Special Issue seeks to include studies making significant advances in the understanding and management of viral hepatitis, liver cancer, and autoimmune liver diseases.

Recent research has underscored the pivotal roles of adipose tissue, gut microbiota, neuroimmune interactions, and regulation of inflammatory cells—including macrophages and neutrophils—in the pathogenesis and progression of liver diseases. We encourage the submission of papers that investigate these novel pathways and translational and clinical studies that may inform future therapeutic strategies across all categories of major liver disease.

By compiling the latest findings in hepatology, this Special Issue seeks to foster interdisciplinary dialogue and provide a platform for innovative research that will advance our understanding and treatment of liver diseases in all their complexity.

Dr. Yuanwen Chen
Guest Editor

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Keywords

metabolic dysfunction-associated steatotic liver disease (MASLD)
metabolic dysfunction-associated steatohepatitis (MASH)
alcohol-associated liver disease (ALD)
alcohol-associated hepatitis
viral hepatitis
liver cancer
autoimmune liver diseases
liver fibrosis
gut microbiota
neuroimmune
inflammatory mechanisms
oxidative stress
reversibly oxidized human non-mercaptalbumin-1
irreversibly oxidized human non-mercaptalbumin-2 (HNA2)

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22 pages, 2901 KB

Open AccessArticle

AI-Driven Radiomics Assisted Prognostic Modeling for Hepatocellular Carcinoma with Portal Vein Invasion: A Retrospective Study

by Tao Zhang, Xue Li, Yingli Guo, Junsong Zeng, Maosen Xu and Yan Tie

Biomedicines 2026, 14(9), 1894; https://doi.org/10.3390/biomedicines14091894 - 25 Aug 2026

Abstract

Background: Portal vein tumor thrombus (PVTT) marks advanced hepatocellular carcinoma (HCC) and carries a dismal prognosis. Survival varies widely even within this stage, yet simple tools for individualized risk stratification remain scarce. Methods: We retrospectively enrolled 134 HCC patients with PVTT and randomly divided them into a training set (n = 94) and a validation set (n = 40). Clinical predictors were selected by variance inflation factor screening and backward elimination Cox regression. A radiomics score (Rad-score) was constructed from portal-venous phase computed tomography (CT) images using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression with 10-fold cross-validation. Three Cox models were built: a clinical model, an imaging model based solely on the Rad-score, and a combined model integrating both. Discrimination was assessed by C-index and time-dependent area under the curve (AUC). Calibration was examined with bootstrap-based calibration curves. Decision curve analysis evaluated net benefit. A nomogram was developed from the combined model. Results: Four clinical variables (alpha-fetoprotein (AFP), body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and alkaline phosphatase (ALP)) and two CT texture features (GLRLM_SRHGE and GLZLM_SZHGE) were retained as independent predictors. The combined model gave the highest C-index in both the training set (0.843) and the internal validation set (0.815). Its 1-year AUC reached 0.953 and 0.947 in the two sets. Calibration slopes ranged from 1.044 to 1.291 across time points, indicating a tendency toward mild overdispersion; nevertheless, decision curve analysis confirmed net benefit across clinically relevant thresholds. The combined model offered greater net benefit than either single-domain model across a 0–50% threshold range. A nomogram incorporating all five predictors was generated for individualized 12- and 24-month survival prediction. Conclusions: A combined model integrating routine laboratory variables and a CT-based radiomics score improved survival prediction over clinical or imaging models alone. The corresponding nomogram uses inputs from a basic blood panel and a single portal-venous phase CT, suggesting its potential as a low-cost prognostic stratification tool for HCC patients with PVTT, although external validation in prospective multicenter cohorts is required before clinical implementation. Full article

(This article belongs to the Special Issue Advances in Hepatology (2nd Edition))

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