Fig. 2

Workflow for the radiomics process. After CT images were acquired, segmentation of liver parenchyma was performed. The extracted radiomics features include intensity, shape, texture features, and wavelet features. Nine radiomics features were selected by the LASSO algorithm. A nomogram was built that incorporates radiomics signature and independent clinical predictors for individualized predicting severe PHLF. The discrimination ability of nomogram and conventional models were compared by ROC curve analysis and quantified by the AUC values. A decision tree was built to stratify the risk for severe PHLF into three classes. Clinical benefits of nomogram and conventional models were compared by decision curve analysis