Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 28, 2024; 30(36): 4044-4056
Published online Sep 28, 2024. doi: 10.3748/wjg.v30.i36.4044
Computed tomography-based multi-organ radiomics nomogram model for predicting the risk of esophagogastric variceal bleeding in cirrhosis
Yu-Jie Peng, Xin Liu, Ying Liu, Xue Tang, Qi-Peng Zhao, Yong Du
Yu-Jie Peng, Xin Liu, Ying Liu, Xue Tang, Qi-Peng Zhao, Yong Du, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Yu-Jie Peng, Xin Liu, Department of Radiology, The People’s Hospital of Chongqing Liang Jiang New Area, Chongqing 401121, China
Author contributions: Peng YJ designed research, performed research, analyzed data and wrote the paper; Liu X performed research and analyzed data; Liu Y contributed analytic tools; Tang X data acquisition; Zhao QP data acquisition; Du Y designed research, project administration and making critical revisions. All authors read and agreed to the published version of the manuscript.
Institutional review board statement: This study was approved by the institutional review board of the Affiliated Hospital of North Sichuan Medical College (IRB-2022ER432-1).
Informed consent statement: This study was approved by the institutional review board of the Affiliated Hospital of North Sichuan Medical College and the requirement for patient-informed consent was waived for this retrospective analysis.
Conflict-of-interest statement: All authors declare no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yong Du, MD, Professor, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuannan Road, Nanchong 637000, Sichuan Province, China. duyong@nsmc.edu.cn
Received: July 2, 2024
Revised: August 28, 2024
Accepted: September 3, 2024
Published online: September 28, 2024
Processing time: 79 Days and 21.3 Hours
Abstract
BACKGROUND

Radiomics has been used in the diagnosis of cirrhosis and prediction of its associated complications. However, most current studies predict the risk of esophageal variceal bleeding (EVB) based on image features at a single level, which results in incomplete data. Few studies have explored the use of global multi-organ radiomics for non-invasive prediction of EVB secondary to cirrhosis.

AIM

To develop a model based on clinical and multi-organ radiomic features to predict the risk of first-instance secondary EVB in patients with cirrhosis.

METHODS

In this study, 208 patients with cirrhosis were retrospectively evaluated and randomly split into training (n = 145) and validation (n = 63) cohorts. Three areas were chosen as regions of interest for extraction of multi-organ radiomic features: The whole liver, whole spleen, and lower esophagus–gastric fundus region. In the training cohort, radiomic score (Rad-score) was created by screening radiomic features using the inter-observer and intra-observer correlation coefficients and the least absolute shrinkage and selection operator method. Independent clinical risk factors were selected using multivariate logistic regression analyses. The radiomic features and clinical risk variables were combined to create a new radiomics-clinical model (RC model). The established models were validated using the validation cohort.

RESULTS

The RC model yielded the best predictive performance and accurately predicted the EVB risk of patients with cirrhosis. Ascites, portal vein thrombosis, and plasma prothrombin time were identified as independent clinical risk factors. The area under the receiver operating characteristic curve (AUC) values for the RC model, Rad-score (liver + spleen + esophagus), Rad-score (liver), Rad-score (spleen), Rad-score (esophagus), and clinical model in the training cohort were 0.951, 0.930, 0.801, 0.831, 0.864, and 0.727, respectively. The corresponding AUC values in the validation cohort were 0.930, 0.886, 0.763, 0.792, 0.857, and 0.692.

CONCLUSION

In patients with cirrhosis, combined multi-organ radiomics and clinical model can be used to non-invasively predict the probability of the first secondary EVB.

Keywords: Artificial intelligence; Cirrhosis; Radiomics; Esophagogastric variceal bleeding

Core Tip: Based on enhanced computed tomography images of patients with cirrhosis and independent clinical risk factors, we developed a new multi-organ combined radiomic model to predict the risk of esophageal variceal bleeding (EVB) in cirrhotic patients, and analyzed its reproducibility and clinical applicability. This new multi-organ combined prediction model can effectively predict the risk of secondary EVB in cirrhosis and provide some support for patient individualized treatment.