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

Table 1 Discriminative performance of peritumoral tissues with different radial dilation distances on lung cancer and pulmonary tuberculosis

Dilations	Cohorts	AUC	Specificity	Sensitivity
0.0 mm	Training cohort	0.875	0.761	0.846
	Validation cohort	0.832	0.791	0.769
1.0 mm	Training cohort	0.875	0.791	0.819
	Validation cohort	0.797	0.884	0.635
2.0 mm	Training cohort	0.889	0.751	0.879
	Validation cohort	0.810	0.907	0.635
3.0 mm	Training cohort	0.900	0.811	0.846
	Validation cohort	0.865	0.907	0.635
4.0 mm	Training cohort	0.914	0.796	0.890
	Validation cohort	0.900	0.907	0.788
5.0 mm	Training cohort	0.888	0.818	0.816
	Validation cohort	0.779	0.652	0.837
6.0 mm	Training cohort	0.899	0.843	0.810
	Validation cohort	0.823	0.804	0.775
7.0 mm	Training cohort	0.897	0.854	0.800
	Validation cohort	0.819	0.630	0.939
8.0 mm	Training cohort	0.906	0.737	0.924
	Validation cohort	0.840	0.739	0.837
9.0 mm	Training cohort	0.904	0.737	0.930
	Validation cohort	0.836	0.739	0.837
10.0 mm	Training cohort	0.906	0.727	0.935
	Validation cohort	0.836	0.739	0.837

AUC: Area under the curve.

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.