Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI

Figure 2

Flowchart of the radiomics analysis. (a) Image data acquisition included CE-T1w, T2flair, T2w, and T1w sequences. And then, data preprocessing: coregistration, reslice, and normalization. (b) The volume of interests (VOIs) of the tumor lesion and peritumoral edema regions were drawn by semiautomatic segmentation. (c) Radiomics features were extracted, including first-order feature, shape-based feature, texture feature, and wavelet feature. (d) Discriminative features were selected by the LASSO regression analysis. (e) The model was trained by radiomics features, clinical features, MRS features, and the percentage of necrotic volume. (f) Radiomics nomogram was established for predicting TERT promoter mutations in adults with high-grade gliomas. The ROC, calibration, and DCA curves were performed for further statistical analyses.