ArticleEuropean radiology2021
An index based on deep learning-measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 37 citations in OpenAlex.
- Missed Opportunities in the Prevention of First Esophageal Variceal Bleeding: A Real-World Clinical Audit of Undiagnosed and Under-Treated Patients.Journal of clinical medicine · 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
- Adding Estimates of Central Venous Pressure Boosts the Performance of Non-Invasive Assessment of the Portosystemic Gradient Prior to TIPS Implantation.Diagnostics (Basel, Switzerland) · 2026Article
- CT-based hepatic and splenic extracellular volume fraction and iodine washout rate for noninvasive assessment of portal hypertension: a preliminary study.BMC medical imaging · 2026Article
- Deep-Learning-based Automatic Segmentation and Quantitative Measurement of Normal Spleen in Chinese AdultsCurrent medical imaging · 2026Article
- Noninvasive model based on liver and spleen stiffness for predicting clinical decompensation in patients with cirrhosis.World journal of gastroenterology · 2025Article
- CT-based detection of clinically significant portal hypertension predicts post-hepatectomy outcomes in hepatocellular carcinoma.European radiology · 2025Article
- Application of artificial intelligence in portal hypertension and esophagogastric varices.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
- Usefulness of Spleen Index and Alkaline Phosphatase Level for Predicting Post-Liver Biopsy Bleeding.JGH open : an open access journal of gastroenterology and hepatology · 2025Article
- Visualizing global progress and challenges in esophagogastric variceal bleeding.World journal of gastrointestinal surgery · 2025Article
- Predictive value of high-risk esophageal varices in cirrhosis based on dual-energy CT combined with clinical and serologic features.BMC medical imaging · 2025Article
- Development of a nomogram model based on spleen volume change to predict high-risk esophageal varices in patients with liver cirrhosis.Frontiers in surgery · 2025Article
- Artificial intelligence-based evaluation of prognosis in cirrhosis.Journal of translational medicine · 2024Review
- Applications of Artificial Intelligence-Based Systems in the Management of Esophageal Varices.Journal of personalized medicine · 2024Review
- The impact of hepatic and splenic volumetric assessment in imaging for chronic liver disease: a narrative review.Insights into imaging · 2024Review
- Changes in Posttreatment Spleen Volume Associated with Immunotherapy Outcomes for Advanced Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2024Article
- Article
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
objectivesDeep learning enables an automated liver and spleen volume measurements on CT. The purpose of this study was to develop an index combining liver and spleen volumes and clinical factors for detecting high-risk varices in B-viral compensated cirrhosis.
methodsThis retrospective study included 419 patients with B-viral compensated cirrhosis who underwent endoscopy and CT from 2007 to 2008 (derivation cohort, n = 239) and from 2009 to 2010 (validation cohort, n = 180). The liver and spleen volumes were measured on CT images using a deep learning algorithm. Multivariable logistic regression analysis of the derivation cohort developed an index to detect endoscopically confirmed high-risk varix. The cumulative 5-year risk of varix bleeding was evaluated with patients stratified by their index values.
resultsThe index of spleen volume-to-platelet ratio was devised from the derivation cohort. In the validation cohort, the cutoff index value for balanced sensitivity and specificity (> 3.78) resulted in the sensitivity of 69.4% and the specificity of 78.5% for detecting high-risk varix, and the cutoff index value for high sensitivity (> 1.63) detected all high-risk varices. The index stratified all patients into the low (index value ≤ 1.63; n = 118), intermediate (n = 162), and high (index value > 3.78; n = 139) risk groups with cumulative 5-year incidences of varix bleeding of 0%, 1.0%, and 12.0%, respectively (p < .001).
conclusionThe spleen volume-to-platelet ratio obtained using deep learning-based CT analysis is useful to detect high-risk varices and to assess the risk of varix bleeding. KEY POINTS: • The criterion of spleen volume to platelet > 1.63 detected all high-risk varices in the validation cohort, while the absence of visible varix did not exclude all high-risk varices. • Visual varix grade ≥ 2 detected high-risk varix with a high specificity (96.5-100%). • Combining spleen volume-to-platelet ratio ≤ 1.63 and visual varix grade of 0 identified low-risk patients who had no high-risk varix and varix bleeding on 5-year follow-up.
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