Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

Table 1 Clinicopathological characteristics of patients with colorectal cancer, n (%)

Variables	Overall (n = 392)
Age, yr
≥ 60	215 (54.8)
< 60	177 (45.2)
Sex
Male	194 (49.5)
Female	198 (50.5)
BMI, kg/m2
≤ 18.5	101 (25.8)
18.5-23.9	89 (22.7)
24.0-27.9	104 (26.5)
≥ 28.0	98 (25.0)
Smoking
Yes	201 (51.3)
No	191 (48.7)
Drinking
Yes	222 (56.6)
No	170 (43.4)
Intestinal polyp
Yes	180 (45.9)
No	212 (54.1)
AST, U/L
< 40	203 (51.8)
≥ 40	189 (48.2)
ALT, U/L
< 50	179 (45.7)
≥ 50	213 (54.3)
Hypertension
Yes	180 (45.9)
No	212 (54.1)
Diabetes
Yes	183 (46.7)
No	209 (53.3)
CEA, ng/mL
Normal	216 (55.1)
Abnormal	176 (44.9)
CA199, U/mL
Normal	194 (49.5)
Abnormal	198 (50.5)
AFP, ng/mL
≤ 100	191 (48.7)
> 100	201 (51.3)
HbsAg
Yes	203 (51.8)
No	189 (48.2)
Tumor type
Adenocarcinoma	211 (53.8)
Mucinous adenocarcinoma	181 (46.2)
Tumor size, cm
< 5	204 (52.0)
≥ 5	188 (48.0)
NI
Yes	180 (45.9)
No	212 (54.1)
VI
Yes	134 (34.2)
No	258 (65.8)
Energy, median [IQR]	3.91 [2.55, 5.62]
SOS, median [IQR]	0.88 [0.69, 1.05]
IND, median [IQR]	1.46 [1.17, 1.80]
MES, median [IQR]	2.84 [1.94, 3.36]
SUV, median [IQR]	20.90 [16.28, 25.33]
SUE, median [IQR]	22.20 [17.10, 27.10]
DIV, median [IQR]	87.50 [67.00, 107.00]
Contrast, median [IQR]	291.00 [275.00, 308.00]
Correlation, median [IQR]	16.13 [12.00, 19.22]
Entropy, median [IQR]	2.17 [1.62, 2.64]
DIE, median [IQR]	230.00 [188.00, 276.00]

IQR: Inter-quartile range; BMI: Body mass index; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; CEA: Carcinoembryonic antigen; CA199: Carbohydrate antigen199; AFP: Alpha-fetoprotein; NI: Neural infiltration; VI: Vascular invasion; SOS: Sum of squares; IND: Inverse difference; MES: Mean sum; SUV: Sum variance; SUE: Sum entropy; DIV: Difference variance; DIE: Difference entropy; HbsAg: Hepatitis B surface antigen.

Table 2 Comparison of predictive efficacy of pulmonary infection prediction models via receiver operating characteristic curves

Model	Training set			Internal validation set
	AUC mean	AUC 95%CI	Variables1	AUC mean	AUC 95%CI	Variables1
RFM	0.917	0.866-0.968	7	0.909	0.858-0.960	7
ANNM	0.796	0.745-0.847	7	0.806	0.755-0.857	7
GLRM	0.783	0.732-0.834	6	0.739	0.688-0.790	6

¹Variables included in the model.

RFM: Random forest model; GLRM: Generalized linear regression model; AUC: Area under the curve; 95%CI: 95% confidence interval; ANNM: Artificial neural network model.