Segmentation, feature extraction, and selection
PTB
REGRESSION, EVENTS
The CT images were imported into the open-source software 3D-Slicer (version 5.0.2, Pyradiomics in 3D-Slicer software was used to extract the radiomics features. The detailed extraction customization was as follows: 1) Feature classes: All features; 2) Resampling and filtering: Resampled voxel size (3,3,3), LoG kernel sizes (4,5), and Wavelet-based features (√). A total of 1037 features were extracted, including 17 histogram classes, 14 form factor classes, 24 Gy level cooccurrence matrix (GLCM) classes, 16 Gy level run length matrix (GLRLM) classes, 16 Gy level size zone matrix (GLSZM) classes, 5 neighboring gray tone difference matrix (NGTDM) classes, and 14 Gy level dependence matrix (GLDM) classes.To reduce the dimensionality of the radiomic features to the number of events, we performed three sequential steps for radiomic feature selection. First, we evaluated the interobserver agreement of radiomic features and selected features showing ICC > 0.75. For the next step, we chose radiomic features that showed statistical significance between the PNMA and PTB groups. Finally, the least absolute shrinkage and selection operator (LASSO) logistic regression model was used to choose the most useful predictive features of radiomics for the differentiation of PNMA from PTB in the training group: fivefold cross validation was performed 100 times to avoid overfitting.