Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 14, 2023; 29(6): 1076-1089
Published online Feb 14, 2023. doi: 10.3748/wjg.v29.i6.1076
Clinical-radiomics nomogram for predicting esophagogastric variceal bleeding risk noninvasively in patients with cirrhosis
Rui Luo, Jian Gao, Wei Gan, Wei-Bo Xie
Rui Luo, Jian Gao, Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, Chongqing, China
Wei Gan, Wei-Bo Xie, Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, Chongqing, China
Author contributions: Luo R was responsible for designing the study, collecting data, analyzing data, and writing the paper; Gao J was responsible for designing the study and guiding important content of the article; Gan W and Xie WB were responsible for providing technical support. All authors approved the final version of the manuscript for submission for publication.
Supported by Kuanren Talents Program of The Second Affiliated Hospital of Chongqing Medical University.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University (No. 2022-149).
Informed consent statement: Our study was a retrospective study conducted at a single center. We used data from patients at the time they were treated in the hospital, and these data were collected and analyzed anonymously. The informed consent waiver was granted by the Institutional Review Board.
Conflict-of-interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships.
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: Jian Gao, MMed, PhD, Chief Doctor, Professor, Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Chongqing Medical University, No. 76 Linjiang Road, Yuzhong District, Chongqing 400010, Chongqing, China. gaojian@cqmu.edu.cn
Received: October 14, 2022
Peer-review started: October 14, 2022
First decision: December 1, 2022
Revised: December 13, 2022
Accepted: January 29, 2023
Article in press: January 29, 2023
Published online: February 14, 2023
Processing time: 119 Days and 7.1 Hours
Abstract
BACKGROUND

Esophagogastric variceal bleeding (EGVB) is a serious complication of patients with decompensated cirrhosis and is associated with high mortality and morbidity. Early diagnosis and screening of cirrhotic patients at risk for EGVB is crucial. Currently, there is a lack of noninvasive predictive models widely available in clinical practice.

AIM

To develop a nomogram based on clinical variables and radiomics to facilitate the noninvasive prediction of EGVB in cirrhotic patients.

METHODS

A total of 211 cirrhotic patients hospitalized between September 2017 and December 2021 were included in this retrospective study. Patients were divided into training (n = 149) and validation (n = 62) groups at a 7:3 ratio. Participants underwent three-phase computed tomography (CT) scans before endoscopy, and radiomic features were extracted from portal venous phase CT images. The independent sample t-test and least absolute shrinkage and selection operator logistic regression were used to screen out the best features and establish a radiomics signature (RadScore). Univariate and multivariate analyses were performed to determine the independent predictors of EGVB in clinical settings. A noninvasive predictive nomogram for the risk of EGVB was built using independent clinical predictors and RadScore. Receiver operating characteristic, calibration, clinical decision, and clinical impact curves were applied to evaluate the model’s performance.

RESULTS

Albumin (P = 0.001), fibrinogen (P = 0.001), portal vein thrombosis (P = 0.002), aspartate aminotransferase (P = 0.001), and spleen thickness (P = 0.025) were selected as independent clinical predictors of EGVB. RadScore, constructed with five CT features of the liver region and three of the spleen regions, performed well in training (area under the receiver operating characteristic curve (AUC) = 0.817) as well as in validation (AUC = 0.741) cohorts. There was excellent predictive performance in both the training and validation cohorts for the clinical-radiomics model (AUC = 0.925 and 0.912, respectively). Compared with the existing noninvasive models such as ratio of aspartate aminotransferase to platelets and Fibrosis-4 scores, our combined model had better predictive accuracy with the Delong's test less than 0.05. The Nomogram had a good fit in the calibration curve (P > 0.05), and the clinical decision curve further supported its clinical utility.

CONCLUSION

We designed and validated a clinical-radiomics nomogram able to noninvasively predict whether cirrhotic patients will develop EGVB, thus facilitating early diagnosis and treatment.

Keywords: Liver cirrhosis; Variceal bleeding; Radiomics; Nomogram; Diagnosis

Core Tip: Esophagogastric variceal bleeding (EGVB) is a life-threatening complication of liver cirrhosis. Currently, no noninvasive prediction models for bleeding risk are widely used in clinical practice. Our study extracted radiomics features from computed tomography and identified portal vein thrombosis, fibrinogen, aspartate aminotransferase, albumin, and spleen thickness as independent clinical predictors. Consequently, we established a novel clinical-radiomics model for noninvasive prediction of EGVB. This model exhibits superior diagnostic performance and can assess bleeding risk early. It has the potential to facilitate early prevention and treatment of possible bleeding in cirrhotic patients with esophagogastric varices.