Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicine

Table 1 Performance of the Cox proportional hazards model and the random survival forest model

Model	Cohort	C-index	AUC
			1-year	3-year	5-year
CoxPH	Training	0.834 (0.789-0.879)	0.848 (0.763-0.930)	0.881 (0.831-0.932)	0.875 (0.822-0.927)
	Internal	0.871 (0.802-0.940)	0.843 (0.717-0.969)	0.948 (0.892-1.000)	0.990 (0.969-1.000)
	External	0.744 (0.665-0.822)	0.786 (0.622-0.889)	0.834 (0.735-0.934)	0.810 (0.688-0.931)
RSF	Training	0.940 (0.924-0.956)	0.962 (0.938-0.989)	0.979 (0.963-0.995)	0.971 (0.951-0.992)
	Internal	0.870 (0.818-0.921)	0.867 (0.761-0.973)	0.955 (0.899-1.000)	0.986 (0.960-1.000)
	External	0.769 (0.691-0.846)	0.803 (0.608-0.891)	0.895 (0.814-0.976)	0.869 (0.769-0.970)

CoxPH: Cox proportional hazards; RSF: Random survival forest; AUC: Area under receiver operating characteristic curve; C-index: Concordance Index.