Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine

Table 1 Comparison of random survival forests model performance vs 5 other models following Cox proportional hazard format to predict early recurrence

Performance	Cohort	RSF	ERASL	Korean	AJCC TNM	BCLC	Chinese
Model Discrimination: (Harrell’s C-Index)	Training	0.725	0.706	0.658	0.674	0.635	0.684
	Internal	0.762	0.726	0.672	0.711	0.646	0.709
	External	0.747	0.727	0.722	0.711	0.658	0.696
Overall Performance: Time dependent Brier (2 years)	Training	0.147	0.156	0.174	0.160	0.167	0.161
	Internal	0.129	0.143	0.159	0.144	0.154	0.146
	External	0.156	0.162	0.161	0.169	0.180	0.176
Clinical Usefulness: Net benefit at threshold 50%	Training	0.166	0.154	0.093	0.139	0.137	0.137
	Internal	0.121	0.092	0.041	0.095	0.073	0.073
	External	0.206	0.190	0.222	0.185	0.154	0.154

RSF: Random survival forest; ERASL: Early Recurrence After Surgery for Liver tumours; AJCC TNM: American Joint Committee on Cancer tumour-node-metastasis; BCLC: Barcelona Clinic Liver Cancer stage.