Published online Jun 26, 2024. doi: 10.12998/wjcc.v12.i18.3340
Revised: April 17, 2024
Accepted: April 23, 2024
Published online: June 26, 2024
Processing time: 106 Days and 1.8 Hours
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).
To predict the PFS of patients with HCC before operation by building a model with enhanced MRI images.
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 C-index.
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.
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.
Core Tip: In this study, traditional radiomics and deep learning (DL) were used to model hepatocellular carcinoma in order to reflect the heterogeneity of tumors through voxel changes in images. The main purpose of this method is to extract the regions of interest features of the three-phase images of enhanced magnetic resonance imaging obtained before transarterial chemoembolization (TACE), construct the prediction model and nomogram through feature screening and least absolute shrinkage and selection operator regression, to construct a prediction model and nomogram to evaluate the clinical value of DL radiomics omics feature model and to predict progression-free survival after TACE.