Published online Feb 14, 2023. doi: 10.3748/wjg.v29.i6.1076
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
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.
To develop a nomogram based on clinical variables and radiomics to facilitate the noninvasive prediction of EGVB in cirrhotic patients.
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 inde
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.
We designed and validated a clinical-radiomics nomogram able to noninvasively predict whether cirrhotic patients will develop EGVB, thus facilitating early diagnosis and treatment.
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.