Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis

Table 2 Selection of metachronous liver metastasis predictive variables based on logistic regression

Variables	Univariate			Multivariate
	OR	95%CI	P value	OR	95%CI	P value
Age [median (IQR)]	1.02	0.55-1.72	> 0.05
Gender
Male	1.00
Female	0.86	0.23-1.02	> 0.05
BMI, kg/m2 [median (IQR)]	1.21	0.78-1.87	> 0.05
Smoking
Yes	1.00
No	0.79	0.18-1.16	> 0.05
Drinking
Yes	1.00
No	0.81	0.23-2.57	> 0.05
MTD, cm
≤ 4	1.00
> 4	1.16	0.77-3.98	> 0.05
Pathological grade
I-II	1.00
III	1.23	0.91-2.26	> 0.05
Pathological type
Infiltrating	1.00
Ulcerative	0.63	0.12-1.16	> 0.05
Cauliflower	0.78	0.23-1.65	> 0.05
Uplift	1.12	0.88-2.37	> 0.05
MRF
Yes	1.00
No	0.63	0.22-1.19	> 0.05
EMVI
Yes	1.00
No	0.67	0.11-1.59	> 0.05
Node
Yes	1.00
No	0.82	0.41-2.16	> 0.05
Spicule
Yes	1.00
No	0.58	0.21-2.26	> 0.05
AFP, ng/mL [median (IQR)]	1.23	0.88-1.77	> 0.05
CA199, U/mL [median (IQR)]	1.15	0.91-1.65	> 0.05
CEA, ng/mL [median (IQR)]	2.13	1.03-4.57	< 0.05	2.11	1.01-4.12	< 0.05
pT stage
T2	1.00
T3	1.13	0.75-2.16	> 0.05
N stage
N0	1.00
N1	1.13	0.86-1.87	> 0.05
N2	1.21	0.99-2.14	> 0.05
Character 1 [median (IQR)]	1.23	0.77-1.96	< 0.05	1.19	0.87-2.16	< 0.05
Character 2 [median (IQR)]	2.67	1.12-4.13	< 0.05	2.58	1.14-3.99	< 0.05
Character 3 [median (IQR)]	1.58	0.88-2.03	> 0.05
Character 4 [median (IQR)]	1.06	0.56-1.87	< 0.05	1.13	0.61-1.94	< 0.05
Character 5 [median (IQR)]	0.98	0.23-1.56	< 0.05	0.91	0.19-2.06	< 0.05
Character 6 [median (IQR)]	1.87	1.02-2.76	> 0.05
Character 7 [median (IQR)]	2.23	1.11-3.87	< 0.05	2.09	1.05-3.55	< 0.05

OR: Odds ratio; CI: Confidence interval; IQR: Interquartile range; BMI: Body mass index; MTD: Maximum tumor diameter; MRF: Mesenteric fascia; EMVI: Extramural venous invasion; AFP: Alpha fetoprotein; CA199: Carbohydrate antigen 199; CEA: Carcinoembryonic antigen; Character 1: T2_wavelet.LHL_firstorder_RobustMeanAbsoluteDeviation; Character 2: T1_log.sigma10.mm.3D_firstorder_Kurtosis; Character 3: T1_lbp.3D.m1_firstorder_Skewness; Character 4: ADC_wavelet.LHL_gldm_LargeDepenceHighGrayLevelEmphasis; Character 5: ADC_wavelet.HLL_glrlm_LongRunHighGrayLeveEmphasis; Character 6: T1_log.sigma30.mm.3D_glrlm_GrayLevelNonUniformity; Character 7: T1_wavelet.HHL_glszm_GrayLevelNonUniformity.