ArticleHepatology international2022
A novel machine learning-based radiomic model for diagnosing high bleeding risk esophageal varices in cirrhotic patients.
Article in Hepatology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Development of a clinical-CT-radiomics nomogram for predicting endoscopic red color sign in cirrhotic patients with esophageal varices.Abdominal radiology (New York) · 2026Article
- Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding.World journal of hepatology · 2026Article
- The evolution of non-invasive strategies in cirrhosis management-from screening to precision monitoring: Editorial on "Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study".Clinical and molecular hepatology · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Development and validation of machine learning models to predict esophagogastric variceal rebleeding risk in HBV-related cirrhosis after endoscopic treatment: a prospective multicenter study.EClinicalMedicine · 2025Article
- Application of artificial intelligence in portal hypertension and esophagogastric varices.World journal of gastroenterology · 2025Review
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- Development and validation of an interpretable machine learning model for standard spleen volume prediction.Quantitative imaging in medicine and surgery · 2025Article
- Visualizing global progress and challenges in esophagogastric variceal bleeding.World journal of gastrointestinal surgery · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Esophageal varices detection and bleeding risk assessment with artificial intelligence: a systematic review.iGIE : innovation, investigation and insights · 2024Article
- Artificial intelligence-based evaluation of prognosis in cirrhosis.Journal of translational medicine · 2024Review
- Computed tomography-based multi-organ radiomics nomogram model for predicting the risk of esophagogastric variceal bleeding in cirrhosis.World journal of gastroenterology · 2024Article
- Applications of Artificial Intelligence-Based Systems in the Management of Esophageal Varices.Journal of personalized medicine · 2024Review
- Guidelines for the Management of Esophagogastric Variceal Bleeding in Cirrhotic Portal Hypertension.Journal of clinical and translational hepatology · 2023Article
- Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.Diagnostics (Basel, Switzerland) · 2023Article
- An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.European radiology · 2023Article
- Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment.Diagnostics (Basel, Switzerland) · 2023Review
- An interpretable artificial intelligence system for detecting risk factors of gastroesophageal variceal bleeding.NPJ digital medicine · 2022Article
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Authors and funding
11 authors.
Funding
Abstract
BACKGROUND AND
aimTo develop and validate a novel machine learning-based radiomic model (RM) for diagnosing high bleeding risk esophageal varices (HREV) in patients with cirrhosis.
methodsA total of 796 qualified participants were enrolled. In training cohort, 218 cirrhotic patients with mild esophageal varices (EV) and 240 with HREV RM were included to training and internal validation groups. Additionally, 159 and 340 cirrhotic patients with mild EV and HREV RM, respectively, were used for external validation. Interesting regions of liver, spleen, and esophagus were labeled on the portal venous-phase enhanced CT images. RM was assessed by area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, calibration and decision curve analysis (DCA).
resultsThe AUROCs for mild EV RM in training and internal validation were 0.943 and 0.732, sensitivity and specificity were 0.863, 0.773 and 0.763, 0.763, respectively. The AUROC, sensitivity, and specificity were 0.654, 0.773 and 0.632, respectively, in external validation. Interestingly, the AUROCs for HREV RM in training and internal validation were 0.983 and 0.834, sensitivity and specificity were 0.948, 0.916 and 0.977, 0.969, respectively. The related AUROC, sensitivity and specificity were 0.736, 0.690 and 0.762 in external validation. Calibration and DCA indicated RM had good performance. Compared with Baveno VI and its expanded criteria, HREV RM had a higher accuracy and net reclassification improvements that were as high as 49.0% and 32.8%.
conclusionThe present study developed a novel non-invasive RM for diagnosing HREV in cirrhotic patients with high accuracy. However, this RM still needs to be validated by a large multi-center cohort.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.