Gao X, Zhang DY, Wang Y. Future directions in noninvasive prediction of cirrhosis decompensation: An opinion review. World J Gastroenterol 2026; 32(29): 117300 [DOI: 10.3748/wjg.117300]
Corresponding Author of This Article
Yan Wang, MD, Assistant Professor, Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157 Xiwu Road, Xi’an 710004, Shaanxi Province, China. sarrye@163.com
Research Domain of This Article
Gastroenterology & Hepatology
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review-article
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Gao X, Zhang DY, Wang Y. Future directions in noninvasive prediction of cirrhosis decompensation: An opinion review. World J Gastroenterol 2026; 32(29): 117300 [DOI: 10.3748/wjg.117300]
World J Gastroenterol. Aug 7, 2026; 32(29): 117300 Published online Aug 7, 2026. doi: 10.3748/wjg.117300
Future directions in noninvasive prediction of cirrhosis decompensation: An opinion review
Xin Gao, Dan-Yang Zhang, Yan Wang
Xin Gao, Dan-Yang Zhang, Yan Wang, Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, Shaanxi Province, China
Author contributions: Gao X contributed to this work; Gao X and Zhang DY wrote this manuscript; Wang Y revised this manuscript; all of the authors read and approved the final version of the manuscript to be published.
AI contribution statement: No generative AI tool (e.g., ChatGPT) was used to write the scientific content of this manuscript. The only AI-assisted tools used were Grammarly (for grammar and style polishing after the manuscript was fully written by the authors) and DeepL (for initial translation of a few non-English references).
Supported by the Shaanxi Provincial Key Research and Development Plan, No. 2020SF-159.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Corresponding author: Yan Wang, MD, Assistant Professor, Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157 Xiwu Road, Xi’an 710004, Shaanxi Province, China. sarrye@163.com
Received: December 4, 2025 Revised: February 19, 2026 Accepted: April 22, 2026 Published online: August 7, 2026 Processing time: 225 Days and 19.5 Hours
Abstract
Cirrhosis is the terminal stage of chronic liver disease. The characteristic manifestations of decompensated cirrhosis are portal hypertension (PH) and complications arising from liver dysfunction, at which stage patient survival is markedly reduced. Hepatic venous pressure gradient serves as the gold standard for assessing PH, with values reaching or exceeding 10 mmHg defining clinically significant PH. Nevertheless, hepatic venous pressure gradient measurement is invasive and limited by high cost, restricted accessibility, and operator dependence, which hinder its routine clinical application. Liver stiffness and spleen stiffness measured via transient elastography have emerged as essential tools for noninvasive risk stratification in chronic liver disease. However, existing noninvasive predictive models are constrained by single-center retrospective designs, homogeneous etiologies, and insufficient standardization of spleen stiffness measurement, and their applicability in patients with hepatocellular carcinoma remains uncertain. Furthermore, the neglect of competing risks in traditional survival analyses has led to overestimation of hepatocellular carcinoma risk. Although technologies such as radiomics and machine learning show considerable promise, prospective multicenter validation remains necessary. Further progress will depend on standardized multi-etiology and multicenter cohorts, supported by rigorous internal and external validation, to produce a universal noninvasive tool for clinical decision-making.
Core Tip: The noninvasive model integrating liver stiffness and spleen stiffness has demonstrated high accuracy in predicting clinical decompensation in patients with viral cirrhosis. Future research should prioritize high-risk subgroups, particularly patients with hepatocellular carcinoma. Standardized protocols for spleen stiffness measurement must be established, and multistate models should be adopted to better characterize disease progression. Future models that combine clinical complexity with radiomics and artificial intelligence-enhanced surveillance may sharpen personalized risk assessment in decompensated cirrhosis.
