Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer

Figure 6 Comparison of the receiver operating characteristic curves among the preope-ANN model, clinical TNM stage, and pathological TNM stage. A: In the training set, the area under the curve (AUC) values of the preoperative artificial neural network (preope-ANN) model, clinical TNM (cTNM) staging, and pathological TNM (pTNM) staging were 0.820 (0.800-0.838), 0.740 (0.718-0.762), and 0.803 (0.782-0.822), respectively. The comparison of the AUC values in the training set showed that the predictive performance of the preope-ANN was better than that of the cTNM stage (P < 0.05), and similar to that of pTNM stage (P = 0.130). B: In the testing set, the AUC values of the preope-ANN model, cTNM staging, and pTNM staging were 0.790 (0.752-0.825), 0.687 (0.644-0.727), and 0.786 (0.748-0.821), respectively. The comparison of the AUC values in the testing set showed that the prediction performance of the preope-ANN and the pTNM stage was better than that of the cTNM stage (P < 0.05), and the prediction performance of the preope-ANN was similar to that of the pTNM stage (P = 0.858). pTNM: Pathological TNM; cTNM: Clinical TNM; preope-ANN: Preoperative artificial neural network.