Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma

Figure 1 Diagram of generative adversarial networks used to generate super-resolution images from original normal-resolution images. MRI: Magnetic resonance imaging.

Figure 2 Study flowcharts of radiomics analysis. T2WI: T2-weighted imaging; DWI: Diffusion-weighted imaging; PVP: Portal venous phase; NR: Normal-resolution; SR: Super-resolution; MRI: Magnetic resonance imaging.

Figure 3 Comparison of receiver operating characteristic curves between normal-resolution and super-resolution magnetic resonance imaging in training, validation, and test cohorts, respectively. A: Portal venous phase model; B: All-sequence model. “All” including three sequences (T2-weighted imaging, diffusion-weighted imaging, portal venous phase). AUC: Area under the curve; NR: Normal-resolution; SR: Super-resolution; PVP: Portal venous phase.

Figure 4 SHapley Additive exPlanations plot of super-resolution magnetic resonance imaging radiomics model based on XGBoost. SHAP: SHapley Additive exPlanations plot; PVP: Portal venous phase; T2WI: T2-weighted imaging; DWI: Diffusion-weighted imaging.

Figure 5 SHapley Additive exPlanations plot force plots demonstrated the difference between super-resolution and normal-resolution magnetic resonance imaging in distinguishing tumor differentiation in the same patient. NR: Normal-resolution; SR: Super-resolution; MRI: Magnetic resonance imaging.

Figure 6 Prognostic value of the signature from super-resolution magnetic resonance imaging in histopathologic grade of hepatocellular carcinoma. A: Overall survival; B: Recurrence-free survival; PD: Poorly differentiated; nPD: Non-poorly differentiated.