Machine learning model using immune indicators to predict outcomes in early liver cancer

Table 2 Comparison of the performance and discriminative ability between the artificial neural networks model and conventional models

Corhort	Models	1-year AUROC (95%CI)	3-year AUROC (95%CI)	5-year AUROC (95%CI)	C-index
Training	ANN	0.92 (0.88-0.95)	0.87 (0.84-0.91)	0.85 (0.82-0.88)	0.81
	JIS	0.77 (0.70-0.83)	0.71 (0.65-0.78)	0.65 (0.61-0.70)	0.63
	Okuda	0.61 (0.53-0.69)	0.70 (0.65-0.75)	0.66 (0.62-0.70)	0.64
	CUPI	0.75 (0.66-0.83)	0.71 (0.63-0.87)	0.73 (0.69-0.77)	0.68
	CLIP	0.71 (0.63-0.78)	0.73 (0.68-0.78)	0.70 (0.66-0.74)	0.66
Validation	ANN	0.81 (0.71-0.91)	0.79 (0.71-0.87)	0.82 (0.74-0.85)	0.78
	JIS	0.60 (0.50-0.73)	0.66 (0.56-0.76)	0.61 (0.54-0.68)	0.61
	Okuda	0.67 (0.55-0.80)	0.69 (0.60-0.79)	0.69 (0.63-0.76)	0.63
	CUPI	0.78 (0.71-0.85)	0.75 (0.69-0.82)	0.76 (0.70-0.82)	0.68
	CLIP	0.70 (0.59-0.81)	0.71 (0.63-0.79)	0.72 (0.66-0.78)	0.66

ANN: Artificial neural network; RSF: Random survival forest; JIS: Japan integrated staging score; Okuda: Okuda staging system; CUPI: Chinese university prognostic index; CLIP: Cancer of the liver Italian program; AUROC: Area under the receiver operating characteristic.