Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study

Table 3 Weights of least absolute shrinkage and selection operator selected features and training set Z-score parameters

Feature names	Weight	Average	Variance
Intercept	0.250252	-	-
Original_shape_Elongation	0.311096	0.524	0.104
Original_firstorder_10Percentile	0.213349	50.029	15.014
Original_firstorder_interquartilerange	0.157092	29.216	10.044
Original_glcm_maximumprobability	-0.10344	0.304	0.092
Original_gldm_dependenceentropy	0.192143	6.133	0.351
Original_ngtdm_Contrast	-0.03429	0.009	0.006
Original_ngtdm_Strength	-0.00931	0.472	1.371
Log-sigma-1-mm-3D_glcm_InverseVariance	-0.44471	0.310	0.026
Log-sigma-1-mm-3D_glszm_LargeAreaHighGrayLevelEmphasis	0.326205	68429444.000	388518307.356
log-sigma-1-mm-3D_glszm_LargeAreaLowGrayLevelEmphasis	-0.02438	10850.300	46169.980
Log -sigma-2-mm-3D_firstorder_Maximum	0.173416	56.164	27.585
Log -sigma-2-mm-3D_glrlm_RunLengthNonUniformity	-0.11635	1356.938	450.535
Log -sigma-2-mm-3D_glszm_ZoneEntropy	-0.18766	5.256	0.371
Log -sigma-3-mm-3D_firstorder_TotalEnergy	-0.26768	57352644.000	20352814.000
Log -sigma-3-mm-3D_glcm_ClusterProminence	0.123895	1173.183	544.414
Wavelet-LLH_firstorder_Mean	0.15131	9.850	2.798
Wavelet-LLH_firstorder_Skewness	0.088985	-1.869	1.851
Wavelet-LHH_glcm_InverseVariance	-0.38886	0.503	0.005
Wavelet-LHH_gldm_LargeDependenceHighGrayLevelEmphasis	-0.25005	3423.815	3331.008
wavelet-HLL_glszm_GrayLevelNonUniformityNormalized	0.104264	0.159	0.094
Wavelet-HLL_glszm_LargeAreaLowGrayLevelEmphasis	-0.05978	10742.590	83666.610
Wavelet-HLH_glcm_InverseVariance	-0.14526	0.501	0.005
Wavelet-HHL_glszm_GrayLevelNonUniformity	0.087039	18.166	12.923
Wavelet-HHH_firstorder_InterquartileRange	0.284918	6.497	1.234
Wavelet-LLL_glcm_InverseVariance	-0.45374	0.448	0.023
Wavelet-LLL_gldm_DependenceEntropy	-0.27325	7.039	0.328
Wavelet-LLL_glszm_GrayLevelNonUniformity	-0.23569	39.394	14.681