Study design
This is a retrospective single center multi-cohort study. The inclusion criteria were: (1) age18-85 years old, (2) patients diagnosed with cirrhosis, confirmed by histopathology and/or typical symptoms and signs of laboratory and imaging findings, including abdominal ultrasonography[10,11], (3) patients who underwent endoscopy and contrast-enhanced CT scan, the interval time between CT scan and endoscopy should be within 3 months. The exclusion criteria were: (1) patients with non-cirrhotic portal hypertension, (2) patients who previously underwent one of the following therapies: transjugular intrahepatic portosystemic shunt, splenectomy, partial splenic embolization, or endoscopic variceal therapy (endoscopic varicel ligation and injection sclerotherapy), (3) patients diagnosed with primary or metastatic hepatocellular carcinoma or other tumors, (4) pregnant or lactation patients. The study protocol was performed in compliance with the Declaration of Helsinki and approved by the Ethics Committees of Beijing You’an Hospital Capital Medical University (Approved number: 2019-074). Signed informed consent was obtained from each patient for using samples, materials and publication.
Subjects
Total 796 qualified participants were enrolled for study. In training cohort, 391 patients with cirrhosis due to any cause were enrolled between January 2016 to December 2017 for. Among them, patients with mild EV (mild EV, n=120), moderate-severe EV (HREV, n=162) and without EV (non EV, n=109) were included. 109 patients were randomly selected from mild EV group to match patients without EV, 120 patients were randomly selected from HREV group to match patients with mild EV to balance the number of cases. Ultimately, 218 (non EV and mild EV) and 240 (mild EV and HREV) patients were separated into the training and internal validation of mild EV and HREV RM (Figure 1a). Additionally, in external validation cohort, 405 cirrhotic patients with hepatitis B from January 2018 to December 2019 were consecutively enrolled, including mild EV(n=94) or HREV (n=246) or non EV(n=65). 159 (non EV and mild EV) and 340 (mild EV and HREV) patients were respectively used for external validation of mild EV and HREV RM (Figure 1a).
Clinical data and Biochemistry
Clinical data included sex, age, laboratory test results, diagnosis, underlying etiology, and complications of cirrhosis/portal hypertension from the electronic medical record. The etiologies of viral cirrhosis were hepatitis B(serum HBsAg positive) ,hepatitis C(serum anti-HCV and HCV-RNA positive). Cirrhosis due to alcoholic, autoimmune and metabolism-related fatty liver diseases were identified according to guideline[12,13,14]. Platelets counts (PLT) were analyzed with XE-5000 (Sysmex, Kobe, Japan). International normalized ratio (INR) and prothrombin time (PT) were measured by an automatic coagulation analyzer (ACLTOP 700, Instrumentation Laboratory Company, USA). An automatic biochemical analyzer (AU5400, Olympus Company, Tokyo, Japan) was used to measure biochemistry of liver and renal, including serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin (TB), albumin (ALB), and creatinine (Cr).
EV evaluation by endoscopy
The flexible GI endoscope (GIF-CV290 or GIF-CV260, Olympus, Japan) was used. The endoscopy was carried out by the experienced gastrointestinal physician and reports were consistently reviewed by two hepatologists to classify the degree of EV as follows[15]: mild EV: linear or slightly tortuous EV(≤ 3 mm diameter) without red signs (Figure 2a); moderate EV: mild EV with red signs or EV with snake or tortuous bulges (3-10 mm diameter) without red signs (Figure 2b); severe EV: medium EV with red signs, or bead-like, or nodular form or EV(>10 mm), regardless of whether there is a red sign or not (Figure 2c). The moderate-severe EV was regarded as HREV in need of treatment[16].
Contrast-enhanced CT image acquisition
All enrolled patients had abdominal contrast-enhanced abdominal CT using 64-slice CT scanner (GE Lightspeed VCT, GE Healthcare, USA). The following parameters were used-collimation, 0.625 mm; tube voltage, 120 kVp; tube current, 380 mA; slice thickness, 5 mm; pitch, 1.375. All patients received an intravenous, Iopromide (iodine concentration, 370 mg/mL) 100 ml via an antecubital vein at a rate of 3 ml/s using a high-pressure injector. All the patients underwent triple-phase contrast-enhanced scans.
Radiomic features extraction
All radiomic data and development of RM were programmed in the Python 3.5 based on Ubuntu 16.04.4. Firstly, Regions of interest (ROI) were created manually on the portal vein phase of contrast-enhanced CT images (DICOM data). Two hepatologists carefully identified tissues in application(AIMED, Version 2.2.3, Beijing Blot Info & Tech Co. Ltd, China) on each slice and labeled liver, spleen and esophagus as ROI (Figure 2d). Since the lesion parts of EV mostly occured within 5 cm esophagus above the cardia[17], ROI of esophagus were labeled in this area. If there was a different opinion on choosing ROI, it will be decided after the two hepatologists review together. 3D reconstruction with marching cubes algorithm and radiomic features extraction were performed on the marked ROI areas reconstructed as 1×1×1mm voxel. The above reconstruction process could reduce the difference of image pixel spacing and form 3D ROI. Four groups of radiomic features were extracted from the segmented ROI including textural features, statistical features, wavelet features and histogram of oriented gradient (HOG) features. Finally, the evaluator were blinded on the patient's endoscopic results in advance.
Dimension reduction
The extraction of imaging features produced a lot of data, which will reduce the efficiency of models development. To simplify the data analysis, we used principal components analysis (PCA) to make dimension reduction. The process is as following. First, invalid image omics features (such as infinite value, null value, features with variance of 0, etc.) are removed, the remaining features are standardized and a covariance matrix was created to obtain the eigenvalues and eigenvectors; Equation is as follows: Xi’= (Xi - Xmin) / (Xmax - Xmin). Xi’: The image features of the patient standardized in this dimension. Xi: The image features of the patient non-standardized in this dimension. Xmax: The maximum value of this dimension. Xmin: The minimum value of this dimension. Then, sorting the eigenvalues from large to small, selecting the principal components, finding their corresponding eigenvectors, projecting the original dimension data onto the selected eigenvectors, the dimensions of the original data characteristics were reduced, and applying the selected dimension data to represent the original data for processing and analysis .
Machine learning algorithm and development of RM
We tested several classification algorithms diagnosing of mild EV or HREV in training cohort. Finally, support vector machine (SVM) with highest AUC and ACC was selected to establish the model. The basic purpose of SVM is to find the best separating hyperplane on the feature space so that the interval between positive and negative samples in the training cohort is maximized. The aim of this algorithm is to minimize the function value:
is regularization term, which is used to reduce the influence of overfitting. The equation is the essential of algorithm to find hyperplane for classification. 5-fold cross-validation were applied to evaluate models and optimize parameters Figure 1b. The participants were randomly divided into 5 parts, one part was used as training and development of RM, and the other 4 parts was used as internal validation. Five models could be obtained and efficiency evaluation was reflected in the form of average value. The RM with minimum mean squared error (MSE) in 5-fold internal cross-validation was selected as the optimization. After retraining optimal RM with all participants, the final RM was obtained which used for external validation.
Efficacy evaluation of RM
AUROC, sensitivity, specificity, accuracy were calculated as indicators to evaluate the discrimination of RM. Calibration curve reflects the agreement between predictions from the models and observed outcomes, and decision curve analysis (DCA) was applied to determine the clinical usefulness of the RM net benefits at different threshold probabilities. Baveno VI consensus proposed that cirrhotic patients with LSM< 20kpa and PLT>150*109 /L had low EVB risks and could avoid endoscope tests.16 The expanded Baveno VI criteria extended the threshold to LSM< 25kpa and PLT>110*109 /L[18]. In other words, people who exceed these criteria are at risk of EV bleeding and should be screened with gastroscopy. We would evaluate the efficacy of RM diagnosing HREV by accuracy and net reclassification improvement (NRI) in external validation cohort compared with Baveno VI, expanded Baveno VI criteria respectively.
Statistical analysis
Statistical Package for Social Sciences (SPSS, version 26.0, IBM Corp.; Armonk, NY, USA) was used for analysis data. The mean and standard deviation(SD) were expressed for normally distributed quantitative data. The median and inter-quartile range were expressed in non-normally distributed data. Normally distributed data matrixes were compared by the independent Student’s t test, while non-normally distributed data matrixes were compared by Mann-Whitney U test. Frequencies and proportions were used to summarize enumeration data with Chi-square test. All authors had access to the study data and reviewed and approved the final manuscript.