Accurate preoperative assessment of microvascular invasion (MVI) can help clinicians choose more reasonable treatment options, reduce the recurrence of HCC patients after surgical treatment, and improve the survival rate of patients. In this study, Graph convolutional network (GCN) was used to build a preoperative diagnostic model for MVI for mining the correlation between radiomic features. The results revealed that the value of the predicted MVI nomogram established was 0.884 in the validation when the radiographic characteristics of the patients were combined with graph convolutional network Score（GS).
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