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Fig. 1 | World Journal of Surgical Oncology

Fig. 1

From: An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver

Fig. 1

Workflow of this study. Firstly, manual segmentation was performed on arterial and portal venous phases MR image. Secondly, image preprocessing and feature extraction are carried out in the volume of interest (VOIs), including seven common feature groups: first order, shape, GLDM, GLCM, GLRLM, GLSZM, NGTDM. Thirdly, in training set, random forest algorithm and MRMR algorithm were used for pre-screening, and then, correlation analysis and LASSO regression were performed to screen out key features for modeling. Finally, three models were established: Clinical Model, Radiomics Model and Combined Model, and model performance were evaluated in validation set. Note: GLDM gray-level dependence matrix, GLCM gray-level cooccurence matrix, GLRLM gray-level run length matrix, GLSZM gray-level size zone matrix, NGTDM neigboring gray tone difference matrix, mRMR Max-Relevance and Min-Redundancy, LASSO the least absolute shrinkage and selection operator algorithm

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