Retrospective Study
Copyright ©The Author(s) 2019.
World J Gastroenterol. Nov 21, 2019; 25(43): 6451-6464
Published online Nov 21, 2019. doi: 10.3748/wjg.v25.i43.6451
Table 2 Univariate logistics regression analysis of risk factors for the 3-year survival
VariableTraining set
OR95CIP value
Age (yr)
< 57REF
57-740.380.250.58< 0.001
> 740.630.420.930.020
Sex
MaleREF
Female0.960.721.290.782
BMI
< 18.5REF
18.5-23.53.041.904.85< 0.001
> 23.51.28 3570.951.730.111
CEA
< 2.8REF
2.8-4.80.330.240.45< 0.001
> 4.80.460.310.67< 0.001
CA199
< 15.0REF
15.0-39.20.200.140.29< 0.001
> 39.20.330.220.50< 0.001
AFP
< 2REF< 0.001
2-5.290.780.511.180.240
> 5.30.500.330.740.001
NLR
< 1.91REF
1.91-3.870.320.220.48< 0.001
> 3.870.480.330.69< 0.001
PLR
< 89.5REF
89.5-162.30.260.160.42< 0.001
> 162.30.590.450.78< 0.001
AGR
< 6.59REF
6.59-7.812.111.502.98< 0.001
> 7.811.581.132.200.007
PNI
< 371REF
371-4303.982.626.06< 0.001
> 4301.941.292.920.002
ASA
IREF
II0.440.240.790.006
III-IV0.530.290.970.041
Comorbidity
NoREF
Yes0.990.741.310.933
Primary site
LowerREF
Middle0.300.200.45< 0.001
Upper0.540.350.830.001
Overlapping lesion of the stomach0.470.310.73< 0.001
Tumor size (mm)
< 30REF
30-600.080.060.13< 0.001
> 600.430.310.60< 0.001
cT
T1REF
T20.050.020.13< 0.001
T30.180.110.32< 0.001
T40.240.170.34< 0.001
cN
N0REF
N10.180.120.27< 0.001
N20.280.180.43< 0.001
N30.670.441.0070.054

  • Citation: Que SJ, Chen QY, Qing-Zhong, Liu ZY, Wang JB, Lin JX, Lu J, Cao LL, Lin M, Tu RH, Huang ZN, Lin JL, Zheng HL, Li P, Zheng CH, Huang CM, Xie JW. Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer. World J Gastroenterol 2019; 25(43): 6451-6464
  • URL: https://www.wjgnet.com/1007-9327/full/v25/i43/6451.htm
  • DOI: https://dx.doi.org/10.3748/wjg.v25.i43.6451