A radiomics model of liver CT to predict risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis. Issue 130 (September 2020)

• Record Type: Journal Article
• Authors: Cao, Jin-ming; Yang, Jian-qiong; Ming, Zhi-qiang; Wu, Jia-long; Yang, Li-qin; Chen, Tian-wu; Li, Rui; Ou, Jing; Zhang, Xiao-ming; Mu, Qi-wen; Li, Hong-jun; Hu, Jiani
• Is Part Of: European journal of radiology. Issue 130(2020)
• Publication Date: 2020-09
• Subjects: HE hepatic encephalopathy -- AASLD American Association for the Study of Liver Diseases -- CECT contrast-enhanced computed tomography -- ROI region of interest -- GLCM gray-level co-occurrence matrix -- GLRLM gray-level run-length matrix -- NGTDM neighboring gray tone difference matrix -- ICC inter-class correlation coefficient -- LASSO least absolute shrinkage and selection operator -- AUC area under receiver-operating characteristic curve; Hepatic encephalopathy -- Liver cirrhosis -- Hepatitis B -- Computed tomography; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2020.109201
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: 19 radiomics features of liver CT were selected to predict hepatic encephalopathy. Radiomics model of liver CT can help predict hepatic encephalopathy. Integrated model of radiomics and clinical features improves the predictive ability. Abstract: Purpose: To build a radiomics model of liver contrast-enhanced computed tomography (CT) to predict hepatic encephalopathy secondary to Hepatitis B related cirrhosis. Materials and Methods: This study consisted of 304 consecutive patients with first-diagnosed hepatitis B related cirrhosis. 212 and 92 patients were randomly computer-generated into training and testing cohorts, among which 38 and 21 patients endured HE, respectively. 356 radiomics features of liver were extracted from portal venous-phase CT data, and 3 clinical features were collected from medical record. 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. Predictive 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 testing cohorts, respectively. Conclusion: The integrated model of radiomics and clinical features could well predict HE secondary to hepatitis B related cirrhosis.