Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans

Table 2 The eight radiomics features selected from the lung computed tomography images

Radiomics features	Cohorts	AUC	P value
Lbp-2D_firstorder_Entropy	Training cohort	0.627	0.000
	Validation cohort	0.618	0.033
Lbp-3D-k_firstorder_10Percentile	Training cohort	0.633	0.022
	Validation cohort	0.568	0.026
Log-sigma-3-0-mm-3D_glcm_Idn	Training cohort	0.557	0.359
	Validation cohort	0.527	0.344
Log-sigma-5-0-mm-3D_glrlm_RunLengthNonUniformity	Training cohort	0.559	0.010
	Validation cohort	0.576	0.329
Squareroot_gldm_DependenceNonUniformity	Training cohort	0.550	0.006
	Validation cohort	0.581	0.404
Wavelet-HLH_glcm_Idn	Training cohort	0.562	0.086
	Validation cohort	0.551	0.304
Wavelet-HLL_glcm_Idn	Training cohort	0.547	0.160
	Validation cohort	0.542	0.435
Wavelet-LLL_glcm_Idmn	Training cohort	0.658	0.000
	Validation cohort	0.663	0.008

AUC: Area under the curve.