Omics-imaging signature-based nomogram to predict the progression-free survival of patients with hepatocellular carcinoma after transcatheter arterial chemoembolization

Figure 1 Flow chart of patient case screening. HCC: Hepatocellular carcinoma; RFA: Radiofrequency ablation; TACE: Transarterial chemoembolization; MRI: Magnetic resonance imaging; AASLD: American Association for the Study of Liver Diseases.

Figure 2 Workflow of radiomics and deep transfer learning. DTL: Deep transfer learning; FC: Fully connected layer; AP: Arterial phase; DP: Delayed phase; PVP: Portal venous phase; DLR: Deep learning radiomics; SVM: Support vector machines; MLP: Multilayer perceptron.

Figure 3 Progression-free survival curves for the high-risk group and low-risk group.

Figure 4 Performance of the models constructed with different radiomic features from various phases. A1: Receiver operating characteristic (ROC) of traditional radiomics; B: ROC of deep transfer learning (DTL); C: ROC of deep learning radiomics (DLR; traditional radiomics +DTL); D: Delong test of traditional radiomics; E: Delong test of deep DTL; F: Delong test of DLR (traditional radiomics +DTL). DTL: Deep transfer learning; AP: Arterial phase; DP: Delayed phase; PVP: Portal venous phase; DLR: Deep learning radiomics; AUC: Area under the curve.

Figure 5 Line charts illustrating predictions of patients' 2-year progression-free survival probabilities using different multiphase radiomic features and multifactorial regression models. DTL: Deep transfer learning; DLR: Deep learning radiomics; PFS: Progression-free survival.