Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma

Table 4 Diagnostic value of the different models in predicting high Ki-67 risk stratification in hepatocellular carcinoma patients

Model	Number of features	Group	Accuracy	Recall	F1	AUC (95%CI)	Sensitivity	Specificity
Clinical	2	Training	0.78	0.705	0.741	0.77 (0.70-0.84)	0.71	0.84
		Validation	0.72	0.737	0.622	0.72 (0.60-0.85)	0.74	0.71
Radiomics	10	Training	0.75	0.705	0.711	0.81 (0.74-0.88)	0.71	0.78
		Validation	0.75	0.474	0.545	0.65 (0.50-0.81)	0.47	0.88
DTL	25	Training	0.80	0.787	0.774	0.87 (0.81-0.92)	0.79	0.81
		Validation	0.57	0.895	0.567	0.67 (0.53-0.82)	0.90	0.42
Nomogram	4	Training	0.84	0.918	0.836	0.92 (0.88-0.97)	0.78	0.95
		Validation	0.68	0.842	0.627	0.75 (0.60-0.88)	0.84	0.61

DTL: Deep transfer learning; AUC: Area under the curve.