Open AccessDevelopment and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study
Author(s) -
Li Ziyu,
Wu Xiaolong,
Gao Xiangyu,
Shan Fei,
Ying Xiangji,
Zhang Yan,
Ji Jiafu
Publication year - 2020
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.3245
Subject(s) - artificial neural network , receiver operating characteristic , multilayer perceptron , medicine , survival analysis , cancer , artificial intelligence , perceptron , cohort , gastrectomy , discriminative model , predictive modelling , prognostic model , computer science , machine learning , statistics , overall survival , mathematics
Abstract Background Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN‐based survival prediction model for patients with gastric cancer. Methods Patients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time‐dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models. Results An ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5‐year survival prediction, respectively. In the calibration curve analysis, the ANN‐predicted survival had a high consistency with the actual survival. Comparison of the DCA and time‐dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts. Conclusions The ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.