Figure 6. Class prediction of HCC patients. (A) Flow chart shows the process of prediction model construction. (B) Confusion matrix evaluations of prediction model within the training cohort and testing cohort. A perfect prediction model (100% accuracy) have 0 counts for all non-diagonal entries (that is, no misclassified samples). (C) ROC curves in training and testing cohort depict trade-offs between true and false positive rates as classification stringency varies. AUC values close to 1 indicate that a high true positive rate was achieved with low false positive rate, while AUC values close to 0.5 indicate random performance. (D) Overview of the characteristics of 4 HCC subclasses. HCC: hepatocellular carcinoma; ROC: Receiver operating characteristic; AUC: Area under the curve.