Development of a clinical-CT-radiomics nomogram for predicting endoscopic red color sign in cirrhotic patients with esophageal varices.

Journal: Abdominal radiology (New York)
Published Date: Sep 27, 2025

Abstract

PURPOSE: To evaluate the predictive performance of a clinical-CT-radiomics nomogram based on radiomics signature and independent clinical-CT predictors for predicting endoscopic red color sign (RC) in cirrhotic patients with esophageal varices (EV). METHODS: We retrospectively evaluated 215 cirrhotic patients. Among them, 108 and 107 cases were positive and negative for endoscopic RC, respectively. Patients were assigned to a training cohort (n = 150) and a validation cohort (n = 65) at a 7:3 ratio. In the training cohort, univariate and multivariate logistic regression analyses were performed on clinical and CT features to develop a clinical-CT model. Radiomic features were extracted from portal venous phase CT images to generate a Radiomic score (Rad-score) and to construct five machine learning models. A combined model was built using clinical-CT predictors and Rad-score through logistic regression. The performance of different models was evaluated using the receiver operating characteristic (ROC) curves and the area under the curve (AUC). RESULTS: The spleen-to-platelet ratio, liver volume, splenic vein diameter, and superior mesenteric vein diameter were independent predictors. Six radiomics features were selected to construct five machine learning models. The adaptive boosting model showed excellent predictive performance, achieving an AUC of 0.964 in the validation cohort, while the combined model achieved the highest predictive accuracy with an AUC of 0.985 in the validation cohort. CONCLUSION: The clinical-CT-radiomics nomogram demonstrates high predictive accuracy for endoscopic RC in cirrhotic patients with EV, which provides a novel tool for non-invasive prediction of esophageal varices bleeding.

Authors

  • Jing Han
    Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education; School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China.
  • Jinghui Dong
    The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Cheng Yan
    Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.
  • Jingwen Zhang
    Department of Communication, University of California, Davis, Davis, CA, United States.
  • Yingxuan Wang
    School of Mathematical Sciences, Ocean University of China, Qingdao, 266000, P. R. China.
  • Mingzi Gao
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Mingxin Zhang
    Department of Urology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266000, China.
  • Yujie Chen
    State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-sen University, 510275 Guangzhou, China.
  • Jianming Cai
    The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China. [email protected].
  • Liqin Zhao
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.

Keywords

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