. 2022 Jun;12(6):3213–3226. doi: 10.21037/qims-21-1004

Table 2. Identification performance of models in IV and EV cohorts.

	IV cohort				EV cohort
	FS3D^U	FS3D^U+H	FS3D^U+A	FS3D^U+H+A	FS3D^U	FS3D^U+H	FS3D^U+A	FS3D^U+H+A
AUC (95% CI)	0.898* (0.780, 1.000)	0.938 (0.844, 1.000)	0.950 (0.865, 1.000)	0.969 (0.901, 1.000)	0.798* (0.668, 0.928)	0.849* (0.734, 0.964)	0.892 (0.793, 0.991)	0.957 (0.894, 1.000)
ACC (95% CI)	0.840 (0.709, 0.928)	0.940 (0.835, 0.988)	0.920 (0.808, 0.978)	0.960 (0.863, 0.995)	0.800 (0.663, 0.900)	0.880 (0.757, 0.955)	0.920 (0.808, 0.978)	0.940 (0.835, 0.988)
SEN (95% CI)	0.838 (0.680, 0.938)	0.946 (0.818, 0.993)	0.919 (0.781, 0.983)	0.973 (0.858, 0.999)	0.862 (0.683, 0.961)	0.966 (0.822, 0.999)	0.966 (0.822, 0.999)	0.966 (0.822, 0.999)
SPE (95% CI)	0.846 (0.546, 0.981)	0.923 (0.640, 0.998)	0.923 (0.640, 0.998)	0.923 (0.640, 0.998)	0.714 (0.478, 0.887)	0.762 (0.528, 0.918)	0.857 (0.637, 0.970)	0.905 (0.696, 10.988)
PPV (95% CI)	0.939 (0.798, 0.993)	0.972 (0.855, 0.999)	0.971 (0.851, 0.999)	0.973 (0.858, 0.999)	0.806 (0.625, 0.926)	0.848 (0.681, 0.949)	0.903 (0.743, 0.980)	0.933 (0.779, 0.992)
NPV (95% CI)	0.647 (0.383, 0.858)	0.857 (0.572, 0.982)	0.800 (0.519, 0.957)	0.923 (0.640, 0.998)	0.789 (0.544, 0.940)	0.941 (0.713, 0.999)	0.947 (0.740, 0.999)	0.950 (0.751, 0.999)
Speed (sec)	10.76	10.76	10.76	10.76	10.76	10.76	10.76	10.76

Comparisons of the AUCs of model FS3D^U+H+Aamong four subgroups were performed by Delong test. *, differences were significant when AUC of FS3D^U+H+A were compared to other models (P<0.05). FS3D^U = FS3D^CEUS; FS3D^U+A = FS3D^CEUS+AFP; FS3D^U+H = FS3D^{CEUS+Hepatitis}; FS3D^U+A+H = FS3D^{CEUS+AFP+Hepatitis}. 95% CI, confidence interval of 95%; IV, internal validation; EV, external validation; AUC, the area under the receiver operating characteristic curve; ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value; FS3D, Four-Stream three-dimensional.