Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors

Table 2

Results of the binomial logistic regression.

Independent variables		Exp(B) with 95% CI
Histology subtype
Reference: adenocarcinoma
(1) Squamous cell carcinoma	<0.001	0.209 (0.089–0.490)
(2) NSCLC (NOS)	0.181	0.443 (0.134–1.461)
(3) SCLC	0.015	0.093 (0.014–0.636)
(4) Others	0.653	0.765 (0.237–2.464)
Location (lobes)
Reference: right upper lobe
(1) Middle lobe	0.350	0.499 (0.116–2.145)
(2) Right lower lobe	0.495	1.446 (0.502–4.167)
(3) Left upper lobe	0.905	1.054 (0.448–2.480)
(4) Left lower lobe	0.902	0.943 (0.369–2.408)
Pleural contact	<0.001	74.400 (9.345–592.324)
Maximal axial diameter	<0.001	0.953 (0.938–0.969)

Detection (yes/no) was set as dependent variable. Independent variables: histology (categorial), location (categorial), pleural contact (dichotomous), and maximal axial diameter (continuous). Exp(B) is the exponentiation of the B coefficient.