摘要
BACKGROUND Enhanced magnetic resonance imaging(MRI)is widely used in the diagnosis,treatment and prognosis of hepatocellular carcinoma(HCC),but it can not effectively reflect the heterogeneity within the tumor and evaluate the effect after treatment.Preoperative imaging analysis of voxel changes can effectively reflect the internal heterogeneity of the tumor and evaluate the progression-free survival(PFS).AIM To predict the PFS of patients with HCC before operation by building a model with enhanced MRI images.METHODS Delineate the regions of interest(ROI)in arterial phase,portal venous phase and delayed phase of enhanced MRI.After extracting the combinatorial features of ROI,the features are fused to obtain deep learning radiomics(DLR)_Sig.DeLong's test was used to evaluate the diagnostic performance of different typological features.K-M analysis was applied to assess PFS in different risk groups,and the discriminative ability of the model was evaluated using the Cindex.RESULTS Tumor diameter and diolame were independent factors influencing the prognosis of PFS.Delong's test revealed multi-phase combined radiomic features had significantly greater area under the curve values than did those of the individual phases(P<0.05).In deep transfer learning(DTL)and DLR,significant differences were observed between the multi-phase and individual phases feature sets(P<0.05).K-M survival analysis revealed a median survival time of high risk group and low risk group was 12.8 and 14.2 months,respectively,and the predicted probabilities of 6 months,1 year and 2 years were 92%,60%,40%and 98%,90%,73%,respectively.The C-index was 0.764,indicating relatively good consistency between the predicted and observed results.DTL and DLR have higher predictive value for 2-year PFS in nomogram.CONCLUSION Based on the multi-temporal characteristics of enhanced MRI and the constructed Nomograph,it provides a new strategy for predicting the PFS of transarterial chemoembolization treatment of HCC.
