A Radiomics Model Of Liver Contrast-enhanced Computed Tomography To Predict Risk Of Hepatic Encephalopathy Secondary To Hepatitis B Related Cirrhosis

Aim:To build a radiomics model of liver contrast-enhanced computed tomography(CT)to predict hepatic encephalopathy secondary to Hepatitis B related cirrhosis.Methods:This study consisted of 304 patients with first-diagnosed hepatitis B related cirrhosis.212 and 92 patients were randomly computer-generated into the training and validation cohorts,among which 38 and 21 patients endured HE,respectively.356 radiomics features of liver were extracted from the venous-phase CT data,and 3 clinical features were sourced from medical record review.After data were standardized by Z-score,we used least absolute shrinkage and selection operator to choose useful radiomics features.Ultimately,three predictive models including a radiomics model,a clinical model and an integrated model of radiomics and clinical features were built by analysis of R-software.The prediction performance was tested by multivariable logistic regression,and evaluated by area under receiver-operating characteristic curve(AUC),and accuracy.Results:19 radiomics features of liver CT were selected.The selected radiomics features and 3 relevant clinical features were applied to develop a radiomics model,a clinical model,and an integrated model of both radiomics and clinical features.The integrated model showed better performance than the radiomics model or clinical model to predict HE(AUC=0.94 vs.0.91 or 0.76,and 0.87 vs.0.86 or 0.73;Accuracy=0.93 vs.0.89 or 0.83,and 0.83 vs.0.84 or 0.77)in the training and validation cohorts,respectively.Conclusion:The integrated model of radiomics and clinical features could well predict HE secondary to hepatitis B related cirrhosis.