Prediction of Remnant Liver Regeneration after Right Hepatectomy in Patients with Hepatocellular Carcinoma Using Preoperative CT Texture Analysis and Clinical Features

OBJECTIVES: To predict the regenerative rate of liver in patients with HCCs after right hepatectomy using texture analysis on preoperative CT combined with clinical features. MATERIALS AND METHODS: 88 patients with 90 HCCs who underwent right hepatectomy were retrospectively included. The future remnant liver was semiautomatically segmented, and the volume of future remnant liver on preoperative CT (LV(pre)) and the volume of remnant liver on following-up CT (LV(fu)) were measured. We calculated the regeneration index (RI) by the following equation: (LV(fu) – LV(pre))/LV(pre)) × 100 (%). The support vector machine recursive method was used for the feature selection. The Naive Bayes classifier was used to predict liver RI, and 5-fold cross-validation was performed to adjust the parameters. Sensitivity, specificity, and accuracy were calculated to evaluate the diagnostic efficiency of the model. RESULTS: The mean RI was 142.99 ± 92.17%. Of all clinical parameters and texture features, the AST, ALB, PT-INR, Perc.10%, and S(5, −5)Correlat were found to be statistically significant with RI. The diagnostic sensitivity, specificity, and accuracy of the model in the training group were 0.902, 0.634, and 0.768, and the AUC value of the obtained model was 0.841. In the test group, the sensitivity, specificity, and accuracy of the model were 1.0, 0.429, and 0.778, respectively, and the AUC value was 0.844. CONCLUSION: The use of texture analysis on preoperative CT combined with clinical features can be helpful in predicting the liver regeneration rate in patients with HCCs after right hepatectomy.