ArticleJHEP reports : innovation in hepatology2022
Spleen volume-based non-invasive tool for predicting hepatic decompensation in people with compensated cirrhosis (CHESS1701).
Article in JHEP reports : innovation in hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Prognostic biomarkers for predicting decompensation in alcoholic and non-alcoholic patients with compensated cirrhosis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Carvedilol to prevent hepatic decompensation of cirrhosis in patients with clinically significant portal hypertension stratified by new non-invasive model (CHESS2306).Clinical and molecular hepatology · 2025Pooled it
- Systematic review and meta-analysis of biomarkers predicting decompensation in patients with compensated cirrhosis.BMJ open gastroenterology · 2024Pooled it
- Dynamics changes of spleen volume predict survival outcomes in unresectable hepatocellular carcinoma treated with tislelizumab plus lenvatinib.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Four-dimensional flow MRI for identifying high-risk esophageal varices: a comparison and combination with spleen volume and splenic extracellular volume fraction.Insights into imaging · 2026Article
- Artificial intelligence prognostication of liver disease using imaging.The British journal of radiology · 2026Review
- Noninvasive Tests for Predicting Decompensation in Compensated Advanced Chronic Liver Disease: A Comprehensive Review.Liver international : official journal of the International Association for the Study of the Liver · 2026Review
- Article
- Development and validation of an interpretable machine learning model for standard spleen volume prediction.Quantitative imaging in medicine and surgery · 2025Article
- Evaluation of gravity effect on liver and spleen volumes using multiposture MRI.Radiological physics and technology · 2025Article
- Artificial intelligence algorithm was used to establish and verify the prediction model of portal hypertension in hepatocellular carcinoma based on clinical parameters and imaging features.Journal of gastrointestinal oncology · 2025Article
- Predictors of Post-Hepatectomy Liver Failure in Klatskin Tumors: The Role of Preoperative Glucose, Future Liver Remnant to Spleen Ratio, and Early Bilirubin Monitoring.Diagnostics (Basel, Switzerland) · 2024Article
- Spleen volume is associated with overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt in patients with portal hypertension.World journal of gastrointestinal surgery · 2024Article
- Prediction of late recurrence after curative-intent resection using MRI-measured spleen volume in patients with hepatocellular carcinoma and cirrhosis.Insights into imaging · 2024Article
- Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection.Clinical and molecular hepatology · 2023Article
- Validation of the albumin-bilirubin score for identifying decompensation risk in patients with compensated cirrhosis.World journal of gastroenterology · 2023Article
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- Prognostic assessment of liver cirrhosis and its complications: current concepts and future perspectives.Frontiers in medicine · 2023Review
- Spleen Area Affects the Performance of the Platelet Count-Based Non-invasive Tools in Predicting First Hepatic Decompensation in Metabolic Dysfunction-Associated Steatotic Liver Disease Cirrhosis.Journal of clinical and experimental hepatologyArticle
Corrections and comments
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Authors and funding
19 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background & Aims: Non-invasive stratification of the liver decompensation risk remains unmet in people with compensated cirrhosis. This study aimed to develop a non-invasive tool (NIT) to predict hepatic decompensation. Methods: This retrospective study recruited 689 people with compensated cirrhosis (median age, 54 years; 441 men) from 5 centres from January 2016 to June 2020. Baseline abdominal computed tomography (CT), clinical features, and liver stiffness were collected, and then the first decompensation was registered during the follow-up. The spleen-based model was designed for predicting decompensation based on a deep learning segmentation network to generate the spleen volume and least absolute shrinkage and selection operator (LASSO)-Cox. The spleen-based model was trained on the training cohort of 282 individuals (Institutions I-III) and was validated in 2 external validation cohorts (97 and 310 individuals from Institutions IV and V, respectively) and compared with the conventional serum-based models and the Baveno VII criteria. Results: The decompensation rate at 3 years was 23%, with a 37.6-month median (IQR 21.1-52.1 months) follow-up. The proposed model showed good performance in predicting decompensation (C-index ≥0.84) and outperformed the serum-based models (C-index comparison test Conclusions: This spleen-based model provides a non-invasive and user-friendly method to help predict decompensation in people with compensated cirrhosis in diverse healthcare settings where liver stiffness is not available. Lay summary: People with compensated cirrhosis with larger spleen volume would have a higher risk of decompensation. We developed a spleen-based model and validated it in external validation cohorts. The proposed model might help predict hepatic decompensation in people with compensated cirrhosis when invasive tools are unavailable
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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.