Table 2

Logistic regression analysis of nodule characteristics and volume cut-offs with lung cancer as outcome

	Univariate analysis		Volume cut-offs added to each characteristic		Full model
	OR (95% CI)	P values	OR (95% CI)	P values	OR (95% CI)	P values	Beta coefficient
Volume cut-off values
<30 mm³	Reference				Reference
30–<200 mm³	7.3 (1.7 to 31.5)	0.008			16.8 (3.2 to 85.4)	0.001	2.818
≥200 mm³	45.1 (10.9 to 186.6)	<0.0001			128.1 (25.1 to 651.9)	<0.0001	4.852
Location
Right upper lung	1.9 (1.2 to 3.1)	0.011	2.1 (1.3 to 3.5)	0.005	2.0 (1.2 to 3.4)	0.012	0.687
Not right upper lung	Reference		Reference		Reference
Right or left lung
Right lung	1.1 (0.7 to 1.8)	0.613	1.0 (0.6 to 1.7)	0.904
Left lung	Reference		Reference
Distribution
Central	2.0 (1.2 to 3.4)	0.005	2.4 (1.4 to 4.1)	0.001	2.4 (1.4 to 4.2)	0.001	0.885
Peripheral	Reference		Reference		Reference
Shape
Spherical	Reference		Reference
Polygonal	1.4 (0.8 to 2.6)	0.235	0.7 (0.4 to 1.4)	0.327
Irregular	4.0 (2.0 to 8.1)	<0.0001	0.8 (0.4 to 1.7)	0.512
Margin
Smooth	Reference		Reference
Lobulated	3.8 (2.0 to 7.0)	<0.0001	1.1 (0.6 to 2.2)	0.722
Spiculated	9.0 (4.2 to 19.4)	<0.0001	1.3 (0.5 to 3.1)	0.576
Irregular	5.4 (2.0 to 14.7)	0.001	0.8 (0.3 to 2.6)	0.880
Visibility in retrospect
Not visible	Reference		Reference		Reference
Small nodule <15 mm³	0.5 (0.2 to 1.0)	0.053	4.7 (1.8 to 12.7)	0.002	4.7 (1.7 to 12.8)	0.003	1.543

Missing values were excluded from analyses. Full model equation: logit(p)=−5.31+volume 30–<200 mm³*2.818+volume >200 mm³*4.825+location in right upper lung*0.687+central distribution*0.885+visibility in retrospect as small nodule <15 mm³*1.543.