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  • Title: Dual-energy computed tomography for non-invasive prediction of the risk of oesophageal variceal bleeding with hepatitis B cirrhosis.
    Author: Liu H, Sun J, Liu X, Liu G, Zhou Q, Deng J, Zhou J.
    Journal: Abdom Radiol (NY); 2021 Nov; 46(11):5190-5200. PubMed ID: 34415412.
    Abstract:
    OBJECTIVE: Oesophageal variceal bleeding (OVB) is a fatal complication of cirrhosis and/or portal hypertension. We aimed to develop a non-invasive prediction model for the risk of OVB using dual-energy computed tomography (CT). METHODS: 317 oesophageal varices (OV) patients with hepatitis B virus-related cirrhosis were retrospectively assessed from January 2018 to December 2018. All patients underwent dual-energy CT scans within 14 days before endoscopy. 222 of 317 patients (174 OVB-negative patients and 48 OVB-positive patients) were included in the training cohort and 95 patients (74 OVB-negative patients and 21 OVB-positive patients) were included in the validation cohort chronologically. A model with the selected conventional CT features and a model with the conventional CT and dual-energy CT features were developed. The prediction accuracy was evaluated using the receiver operating characteristic (ROC) curve. The accuracy and reproducibility of the models for OVB risk prediction of cirrhosis were validated by the validation cohort. The areas under the curve (AUC) of the two models were compared with Delong test. RESULTS: Diameter of oesophageal vein (OV(mm)), diameter of splenic vein (SPV(mm)), ascites (AS), iodine concentration in short gastric vein (SGV(HU)), iodine concentration in spleen (SP(HU)) were independent predictors of OVB risk (P < 0.05). Then, we developed a model with the selected conventional CT features [OV(mm), SPV(mm), AS] and a model with the conventional CT and dual-energy CT features [OV(mm), SPV(mm), AS, SGV(HU), SP(HU)]. The AUCs of the model built with the conventional CT and dual-energy CT features were higher than the model built only with the conventional CT features in the training (0.839 vs 0.809) and validation cohorts (0.798 vs 0.738). CONCLUSION: The non-invasive prediction model developed with the conventional CT and dual-energy CT features may have added value in noninvasively predicting OVB than the model built only with the conventional CT features and may have significant clinical implications on early prevention and treatment of OVB. ADVANCES IN KNOWLEDGE: Combination of dual-energy CT with conventional CT may have added value for non-invasive prediction of OVB compared to conventional CT.
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