Table 3.

Description and Significance of Radiomic Features Analyzed

Feature	Description	Significance
First-order statistics	Mean, standard deviation, median, and range; first-order differentials computed using Sobel operators	Localize hypo- and hyperintense regions; gradients detect edges and quantify region boundaries
Co-occurrence features	Localization of regions with significant intensity changes; gradients detect edges and quantify region boundaries	Localizes regions based on underlying heterogeneity of voxel intensities
CoLlAGe features	Localization of regions with significant local oriented gradient changes;	Localizes regions based on underlying heterogeneity of oriented voxel gradients
Gabor features	Convolution of Gaussian function with a Fourier transform at different orientations and frequencies	Quantify the appearance of cancer lesions at multiple orientations and image scales
Laws texture features	Computed by convolution of the image with local masks obtained from vectors that capture local average, edge, spot, wave, and ripple patterns	Quantify the variation of pixel intensities within a fixed region of the image; regions of image containing cancer lesions typically contain lower texture energy