Performance of artificial intelligence in predicting hepatocellular carcinoma recurrence after thermal ablation: A systematic review

Table 1 Characteristics of the included studies¹

Ref.	Study type	Patient number	Model	AUC	Sensitivity	Specificity	C-index	PPV	NPV	Accuracy
Lim et al[12]	Retro	74	DL	0.99	96%	97%	N/A	0.91	N/A	97.6%
Sato et al[13]	Retro	1778	ML + C	N/A	N/A	N/A	0.67	N/A	N/A	N/A
Zhang et al[14]	Retro	90	ML + C	0.86	N/A	N/A	N/A	N/A	N/A	N/A
Tabari et al[15]	Retro	97	ML + R + C	0.83	82%	67%	N/A	0.69	0.80	N/A
Peng et al[16]	Retro	149	ML + R + C	N/A	N/A	N/A	0.72	N/A	N/A	N/A
Ren et al[17]	Retro	607	ML + C	0.89	N/A	85%	N/A	N/A	N/A	N/A
Chen et al[18]	Retro	417	DL + R + C	0.87	86%	91%	N/A	0.79	0.90	88%
Wang et al[19]	Retro	535	DL + R + C	0.79	72%	86%	N/A	N/A	N/A	78%
Kong and Li[20]	Retro	289	DL + R + C	0.74	71%	71%	N/A	0.39	0.91	71%
Li et al[21]	Retro	288	DL + R + C	0.98	N/A	N/A	N/A	0.94	0.87	91.6%

¹In case of more than one trained model, the best-performing one was reported.

Retro: Retrospective; DL: Deep learning; ML: Machine learning; R: Radiomic; C: Clinical; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; N/A: Not applicable.