Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer

Table 3 Comparison of the area under the curve values and Harrell’s C index between the pathological TNM stage, clinical TNM stage, and preoperative artificial neural network

	Training set			Testing set			Bio-ANN	Cli-ANN
	Preope-ANN	cTNM	pTNM	Preope-ANN	cTNM	pTNM
Harrell’s C index	0.773 (0.753-0.795)	0.663 (0.640-0.687)	0.757 (0.735-0.779)	0.752 (0.719-0.785)	0.652 (0.615-0.688)	0.740 (0.707-0.775)	0.722 (0.698-0.746)	0.760 (0.738-0.782)
P value		< 0.001	0.120		< 0.001	0.539	aP < 0.001; bP = 0.000; cP = 0.018	dP < 0.001 eP < 0.000; fP = 0.827
AIC	4977.83	5176.70	4999.80	1952.94	2020.37	1951.84	5115.9	5011.9
Relative likelihood		< 0.001	< 0.001		<0.001	1.733	aP < 0.001; bP > 1 cP < 0.001	dP = 0.001 E > 1 fP = 0.06

^aP, Bio-artificial neural network (ANN) vs preoperative ANN (preope-ANN);

^bP, Bio-ANN vs clinical TNM (cTNM);

^cP, Bio-ANN vs pathological TNM (pTNM).

^dP, cli-ANN vs preope-ANN;

^eP, cli-ANN vs cTNM;

^fP, cli-ANN vs pTNM. pTNM: Pathological TNM; cTNM: Clinical TNM; preope-ANN: Preoperative artificial neural network; AIC: Akaike information criterion.