Study design and patients
This retrospective-prospective study was performed in Qilu Hospital of Shandong University (Institution 1) and Jinan Central Hospital (Institution 2). The study design and procedures were presented in full in the study protocol (ClinicalTrials.gov: NCT04210297). Ethical committee approval was granted by the Medical Ethics Committee of involved institutions. The informed consent was waived in the training set for the retrospective analysis. All procedures involving human participants were performed following the Helsinki declaration and its later amendments.
The inclusion criteria were (1) patients who were diagnosed with liver cirrhosis [20]. (2) patients who underwent the abdominal contrast-enhanced CT examination. (3) patients who received endoscopic screening. (4) patients with written informed consent. Exclusion criteria included: (1) patients who previously underwent endoscopic therapy, transjugular intrahepatic portosystemic shunt (TIPS), splenectomy, partial splenic embolization (PSE), hepatectomy, balloon-occluded retrograde transvenous obliteration, or liver transplantation. (2) patients with liver cancer. (3) patients with severe ascites or hepatic encephalopathy. (4) lacks abdominal contrast-enhanced CT within 1 month of endoscopy. The study design and recruitment pathways for patients in this study are shown in Fig. 1.
The training set consists of patients who were retrospectively collected in institution 1 from January 2018 to December 2019. The internal and external validation set consists of patients who were prospectively enrolled from January 2020 in institution 1 and institution 2 respectively.
Upper endoscopic examination
Every patient received an upper endoscopic examination for the screening of EV and identifying the risk of bleeding. Upper endoscopic examination was performed by experienced endoscopists. The endoscopic findings were recorded in a standard format. VNT was defined as small varices (diameter<5mm) with red signs and large varices (diameter>5mm).
Radiomics analysis
The workflow of the radiomics analysis is summarized in Figure 1 and can be divided into four steps: CT image acquisition, region of interest (ROI) segmentation, feature extraction, and radiomics signature construction.
CT image acquisition
Every patient underwent an abdominal enhanced CT scan after an overnight fast using one of the following systems: Discovery CT750 HD (GE Healthcare), Brilliance iCT (Philips Healthcare), or Sensation 16 CT (Siemens). The following parameters were used: tube voltage, 120 or 140kVp; tube current, 150−600 mAs; slice thickness, 1.25 mm; pitch, 1.375. Ultravist (2.5 mL/kg, 300 mg/mL) was injected intravenously at a rate of 3 mL/s. Arterial phase scan began at the 30s after injection, while the venous phase and delayed phase scan were started at 70 and120 s, respectively. Portal venous phase CT images were retrieved from the picture archiving and communication system (PACS).
Region of interest (ROI) segmentation and feature extraction
The liver at the porta hepatis level, the spleen at splenic hilum level, and the level from the lower esophagus to gastric fundus were selected as the ROI. ROI was delineated manually by two radiologists (reader 1: Dexin Yu and reader 2: Zhuyun Liu with 20 and 3 years of clinical experience in abdominal CT interpretation respectively) using the ITK-SNAP 3.8 (www.itksnap.org). The two radiologists were blinded to the endoscopic findings. Radiomic features were extracted from each ROI using the MATLAB 2018b (MathWorks, Natick, USA) by utilizing the open-source radiomics feature extraction package. Textural and non-textural feature extractions were conducted. Image normalization including Wavelet bandpass filtration, isotropic resampling, and quantization of gray level was performed before radiomic features extraction. For each ROI, 10324 radiomic features were extracted and a total of 30972 radiomic features were extracted from each patient. More information about radiomics features extraction methodology is shown in Supplementary information.
The inter-observer and intra-observer reliability were analyzed with 30 randomly chosen cases from the training set, two radiologists repeated ROI segmentation and feature extraction twice on those cases with a one-month interval. The reliability was calculated by using the intra-class correlation coefficient (ICC), both intra-observer and inter-observer ICC values greater than 0.75 were regarded as robust reliability and reproducibility.
Feature selection andRadiomics signature construction
The least absolute shrinkage and selection operator (LASSO) logistic regression method was used to select the most effective predictive features from the training set. The LASSO logistic regression model was used with tuning penalty parameter lambda(λ) that was conducted by 10-fold cross-validation based on minimum criteria. A formula was generated using a linear combination of selected features that were weighted by their respective LASSO coefficients; the formula was then used to calculate a radiomics signature (Rad-score) for each patient to predict the risk of VNT.
Radiomics nomogram construction
The Rad-scores and the clinical variables were tested in univariate logistic regression analysis in the training set. All variables with P < 0.05 were entered into the multivariate logistic regression analysis. A radiomics nomogram was then constructed according to the multivariate logistic regression model.
Statistical analysis
Differences of clinical characteristics between the training set and the validation set as well as between the VNT group and non-VNT group in their respective datasets were assessed using independent sample t-test or Mann-Whitney U-test. The optimal cut-off value for Rad-score was determined using Youden’s index in the training set, which maximizes the sum of sensitivity and specificity. The predictive accuracy of the radiomics signature was quantified by the area under the receiver operator characteristic (ROC) curve (AUC) in both training and validation sets. The likelihood ratio test with a backward stepwise selection was applied to the multivariate logistic regression model. Additionally, a decision curve analysis was performed to evaluate the clinical usefulness and net benefits of the developed radiomics nomogram. Statistical analysis was performed using the R software (version 3.6.2, R Project for Statistical Computing, http://www.r-project.org). Two-sided P-values less than 0.05 were considered statistically significant.