CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn’s disease resistant to infliximab

Insights into Imaging

Table 2 The selected radiomics features of the VAT radiomics model and the corresponding coefficients

Radiomics feature	Coefficient (absolute value)
original_shape_Flatness	0.067293
original_shape_Sphericity	0.047925
wavelet-LHL_glrlm_LongRunLowGrayLevelEmphasis	0.035222
wavelet-HHH_firstorder_Skewness	0.034753
wavelet-HHH_glszm_LowGrayLevelZoneEmphasis	0.032765
wavelet-HLL_glcm_InverseVariance	0.019269
wavelet-HHL_glcm_MCC	0.011879
wavelet-LHL_glcm_JointEnergy	0.008703
wavelet-LLH_firstorder_Minimum	0.005895
wavelet-LHH_glszm_LargeAreaLowGrayLevelEmphasis	0.005520
wavelet-LLH_gldm_LargeDependenceHighGrayLevelEmphasis	0.005261
wavelet-HHH_glrlm_ShortRunLowGrayLevelEmphasis	0.004115

The VAT radiomics model was the model developed based on radiomics features extracted from visceral adipose tissue (VAT). The coefficient of each radiomics feature was generated by the least absolute shrinkage and selection operator algorithm and presented as absolute value. Each feature was named by concatenating the image type from which the feature was extracted, feature group and feature name by underline. For example, original_shape_Flatness was a feature extracted from the original image, shape group, and the feature name was Flatness. Glrlm, gray-level run length matrix; glszm, gray-level size zone matrix; glcm, gray-level co-occurrence matrix; gldm, gray-level dependence matrix; all features above belong to texture features