Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study

Figure 1 A diagram of the research process. ROI: Region of interest.

Figure 2 The process of patient selection. CT: Computed tomography; HCC: Hepatocellular carcinoma.

Figure 3 Comparative analysis of area under the receiver operating characteristic curve for the six models. A: Comparison of area under the receiver operating characteristic curve (AUC) for the six models on the training set; B: Comparison of AUC for the six models on the internal validation set. AUC: Area under the receiver operating characteristic curve; Combined: The model that incorporates features from all regions—tumor, 5 mm, and 10 mm; P5: The 5 mm peri-tumor model; P10: The 10 mm peri-tumor model; T: The tumor-only model; TP5: The tumor-peri-tumor 5 mm model; TP10: The tumor-peri-tumor 10 mm model.

Figure 4 Area under the receiver operating characteristic curve curves for training set, internal validation set, and external validation set. A: Area under the receiver operating characteristic curve (AUC) for the training set; B: AUC for the internal validation set; C: AUC for the external validation set. AUC: Area under the receiver operating characteristic curve; Combined: The model that incorporates features from all regions—tumor, 5 mm, and 10 mm.

Figure 5 Calibration curve and decision curve analysis curve of the model that incorporates features from all regions—tumor, 5 mm, and 10 mm model. A: Represent the calibration curves of the model that incorporates features from all regions—tumor, 5 mm, and 10 mm (combined) model on the training set, internal validation set, and external validation set, respectively; B: Represent the calibration curves of the combined model on the internal validation set; C: Represent the calibration curves of the combined model on the external validation set; D: Represent the decision curve analysis (DCA) curves of the combined model on the training set; E: Represent the DCA curves of the combined model on the internal validation set; F: Represent the DCA curves of the combined model on the external validation set. Combined: The model that incorporates features from all regions—tumor, 5 mm, and 10 mm.

Figure 6 Kaplan-Meier curves for 2-year progression-free survival and 5-year overall survival for the training set, internal validation set, and external validation set. A: Represent Kaplan-Meier (K-M) curves for 2-year progression-free survival (PFS) of the training set; B: Represent K-M curves for 2-year PFS of the internal validation set; C: Represent K-M curves for 2-year PFS of the external validation set; D: Represent K-M curves for 5-year overall survival (OS) of the training set; E: Represent K-M curves for 5-year OS of the internal validation set; F: Represent K-M curves for 5-year OS of the external validation set.