Fig. 3

The workflow of radiomics analysis in the present study. First, VOIs were manually delineated around the entire tumor outline on each axial slice of T2WI-FS and CE-T1WI images. Second, 1688 radiomics features were extracted from each three-dimensional segmentation. Third, three steps of feature selection were applied to all extracted features. Then, seven radiomics signatures were built using seven machine learning classifiers, and the radiomics signature with the best predictive performance was used to build the radiomics model. A clinical model was constructed using logistic regression analysis. Finally, a fusion model incorporating the optimal radiomics score and key clinical characteristics was built and presented as a nomogram, which was evaluated by ROC analysis, calibration curve, and DCA. CE-T1WI, contrast-enhanced T1-weighted imaging; DCA, decision curve analysis; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; T2WI-FS, T2-weighted imaging fat suppression; VOI, volume of interest