. Author manuscript; available in PMC: 2017 Sep 1.

Published in final edited form as: Clin Lung Cancer. 2016 Feb 16;17(5):441–448.e6. doi: 10.1016/j.cllc.2016.02.001

Table 4.

Multiple logistic regression analysis of clinicopathological parameters and radiomic features predicting the presence of EGFR mutation in peripheral lung adenocarcinomas

Model	Features		p-value	Odds Ratio			AUC
				Point	95% CI		Point	95% CI
				Point	Lower	Upper	Point	Lower	Upper
Clinical features	Pathologic grade (ref= Lower or Intermediate)	High	0.002	0.39	0.22	0.70	0.667	0.604	0.721
Clinical features	Smoking status (ref = Never smokers)	Yes	<.0001	0.38	0.23	0.61	0.667	0.604	0.721
Radiomic features	Main direction (F27)		0.115	1.17	0.96	1.42	0.647	0.576	0.701
	3D Laws features L5 L5 S5 Layer 1 (F92)		0.010	0.52	0.31	0.85
	Histogram ENERGY Layer 1 (F186)		0.025	0.50	0.27	0.92
	3D Wavelet decomposition. P2 L2 C9 Layer 1 (F190)		0.048	1.29	1.00	1.67
	3D Wavelet decomposition. P1 L2 C5 Layer 1 (F216)		0.137	1.16	0.96	1.40
Clinical + Radiomic features	Pathologic grade (ref= Lower or Intermediate)	High	0.016	0.47	0.25	0.87	0.709	0.654	0.766
	Smoking status (ref = Never smokers)	Yes	0.001	0.41	0.25	0.67
	Main direction (F27)		0.20	1.14	0.93	1.40
	3D Laws features L5 L5 S5 Layer 1 (F92)		0.056	0.60	0.36	1.01
	Histogram ENERGY Layer 1 (F186)		0.11	0.60	0.32	1.13
	3D Wavelet decomposition. P2 L2 C9 Layer 1 (F190)		0.13	1.22	0.94	1.59
	3D Wavelet decomposition. P1 L2 C5 Layer 1 (F216)		0.24	1.13	0.92	1.38

AUC – area under curve