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Biomedicines
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Advances in Hepatology
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Advances in Hepatology

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 816

Special Issue Editor

Dr. Yuanwen Chen

E-Mail Website
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

Special Issue Information

Dear Colleagues,

This Special Issue, “Advances in Hepatology”, 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 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, the gut microbiota, neuroimmune interactions, and the 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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (1 paper)

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Research

18 pages, 8113 KiB  
Article
An Interpretable Machine Learning Model Based on Inflammatory–Nutritional Biomarkers for Predicting Metachronous Liver Metastases After Colorectal Cancer Surgery
by Hao Zhu, Danyang Shen, Xiaojie Gan and Ding Sun
Biomedicines 2025, 13(7), 1706; https://doi.org/10.3390/biomedicines13071706 - 12 Jul 2025
Viewed by 172
Abstract
Objective: Tumor progression is regulated by systemic immune status, nutritional metabolism, and the inflammatory microenvironment. This study aims to investigate inflammatory–nutritional biomarkers associated with metachronous liver metastasis (MLM) in colorectal cancer (CRC) and develop a machine learning model for accurate prediction. Methods [...] Read more.
Objective: Tumor progression is regulated by systemic immune status, nutritional metabolism, and the inflammatory microenvironment. This study aims to investigate inflammatory–nutritional biomarkers associated with metachronous liver metastasis (MLM) in colorectal cancer (CRC) and develop a machine learning model for accurate prediction. Methods: This study enrolled 680 patients with CRC who underwent curative resection, randomly allocated into a training set (n = 477) and a validation set (n = 203) in a 7:3 ratio. Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. Seven machine learning (ML) models were constructed using the training set, with the optimal predictive model selected based on comprehensive evaluation metrics. An interactive visualization tool was developed to interpret the dynamic impact of key features on individual predictions. The partial dependence plots (PDPs) revealed a potential dose–response relationship between inflammatory–nutritional markers and MLM risk. Results: Among 680 patients with CRC, the cumulative incidence of MLM at 6 months postoperatively was 39.1%. Multimodal feature selection identified nine key predictors, including the N stage, vascular invasion, carcinoembryonic antigen (CEA), systemic immune–inflammation index (SII), albumin–bilirubin index (ALBI), differentiation grade, prognostic nutritional index (PNI), fatty liver, and T stage. The gradient boosting machine (GBM) demonstrated the best overall performance (AUROC: 0.916, sensitivity: 0.772, specificity: 0.871). The generalized additive model (GAM)-fitted SHAP analysis established, for the first time, risk thresholds for four continuous variables (CEA > 8.14 μg/L, PNI < 44.46, SII > 856.36, ALBI > −2.67), confirming their significant association with MLM development. Conclusions: This study developed a GBM model incorporating inflammatory-nutritional biomarkers and clinical features to accurately predict MLM in colorectal cancer. Integrated with dynamic visualization tools, the model enables real-time risk stratification via a freely accessible web calculator, guiding individualized surveillance planning and optimizing clinical decision-making for precision postoperative care. Full article
(This article belongs to the Special Issue Advances in Hepatology)
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