Cancer Imaging

Table 4 Accuracy, sensitivity, specificity and AUC scores of MVI predictive models for training, validation and external sets

From: Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals

Model	Training set				Validation set				External set
Model	Accuracy	Sensitivity	Specificity	AUC	Accuracy	Sensitivity	Specificity	AUC	Accuracy	Sensitivity	Specificity	AUC
ResNet-18(AP)	0.95	0.91	0.97	0.98	0.68	0.96	0.56	0.82	0.66	0.8	0.62	0.75
ResNet-18(AP + CF)	0.97	0.94	0.98	0.97	0.72	0.96	0.62	0.85	0.71	0.82	0.67	0.78
SVM(CF)	0.71	0.82	0.66	0.78	0.77	0.71	0.8	0.78	0.7	0.77	0.67	0.76
SVM(AP + CF)	0.93	0.92	0.93	0.98	0.6	0.93	0.46	0.7	0.57	0.9	0.47	0.68

ResNet-18 model built with AP images and CF generalized well and produced the best metric scores on the external validation set. SVM support vector machine; AP arterial phase; CF clinical factors; AUC area under the curve.

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ISSN: 1470-7330

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