ArticleClinical and molecular hepatology2023
Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection.
Article in Clinical and molecular hepatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 8 citations in OpenAlex.
- Artificial intelligence prognostication of liver disease using imaging.The British journal of radiology · 2026Review
- Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.Abdominal radiology (New York) · 2026Article
- Hepatocellular Carcinoma Risk Stratification for Cirrhosis Patients: Integrating Radiomics and Deep Learning Computed Tomography Signatures of the Liver and Spleen into a Clinical Model.Journal of clinical and translational hepatology · 2025Article
- Decreasing performance of HCC prediction models during antiviral therapy for hepatitis B: what else to keep in mind: Editorial on "Hepatocellular carcinoma prediction model performance decreases with long-term antiviral therapy in chronic hepatitis B patients".Clinical and molecular hepatology · 2024Article
- Correspondence to editorial on "Hepatocellular carcinoma prediction model performance decreases with long-term antiviral therapy in chronic hepatitis B patients".Clinical and molecular hepatology · 2024Article
- Deep learning assisted biomarker development in patients with chronic hepatitis B: Editorial on "Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection".Clinical and molecular hepatology · 2024Article
- Assessment of body composition and prediction of infectious pancreatic necrosis via non-contrast CT radiomics and deep learning.Frontiers in microbiology · 2024Article
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6 authors at 2 institutions in 1 country.
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Abstract
BACKGROUND/
aimsThe prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB.
methodsThis retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses.
resultsDuring a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD.
conclusionDeep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.
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