Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma

Figure 1 Receiver operating characteristic curves and decision curve analysis. A: Receiver operating characteristic curves of clinical, deep learning-based radiomics (DLR), and clinical + DLR models for predicting early recurrence in the training and testing cohorts; B: Decision curve analysis (DCA) of each model in predicting early recurrence. The vertical axis measures standardized net benefit. The horizontal axis shows the corresponding risk threshold. The DCA showed that if the threshold probability is between 0 and 1, using the DLR model derived in the present study to predict ER provided the same benefit as clinical model. ROC: Receiver operating characteristic; DCA: Decision curve analysis; DL: Deep learning; DLR: Deep learning-based radiomics.

Figure 2 Kaplan-Meier curves of overall survival stratified by high and low risk for clinical, deep learning-based radiomics, and clinical + deep learning-based radiomics models. A and B: Clinical training; C and D: Deep learning-based radiomics testing; E and F: Clinical + DLR. DLR: Deep learning-based radiomics.

Figure 3 Relationship between the deep learning-based radiomics model and benefit from retreatment after recurrence in matched patients. A: Four different risk classes were defined by early recurrence and overall survival predicted by the deep learning-based radiomics model; B-E: Kaplan-Meier curves of disease-free survival for patients who were stratified according to receipt of retreatment after recurrence. HR: Hazard ratio.