CT-Based radiomics model for preoperative prediction of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt

Objective: To develop and evaluate a machine learning-based CT radiomics model for the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS). Methods: A total of 106 patients who underwent TIPS placement were consecutively enrolled in this retrospective study. Region of interests (ROIs) were drawn on unenhanced, arterial phase, and portal venous Phase CT images, and radiomics features were extracted, respectively. A radiomics model was established to predict the occurrence of HE after TIPS by using random forest algorithm and ten-fold cross-validation. Receiver operating characteristic (ROC) curves were performed to validate the capability of the radiomics model and clinical model on the training, test and original datasets, respectively. Results: The radiomics model showed favorable discriminatory ability in the training cohort with an area under the curve (AUC) of 0.899 (95% CI, 0.848 to 0.951), while in the test cohort, it was confirmed with an AUC of 0.887 (95% CI, 0.760 to 1.00). After applying this model to original dataset, it had an AUC of 0.955 (95% CI, 0.896 to 1.00). A clinical model was also built with an AUC of 0.649 (95% CI, 0.530 to 0.767) in the original dataset, and a Delong test demonstrated its relative lower efficiency when compared with the radiomics model (p < 0.05). Conclusion: Machine learning-based CT radiomics model performed better than traditional clinical parameter-based models in the prediction of post-TIPS HE. Advances in knowledge: Radiomics model for the prediction of post-TIPS HE was built based on feature extraction from routine acquired preoperative CT images and feature selection by random forest algorithm, which showed satisfied performance and proved the advantages of machine learning in this field.