Construction and verification of artificial intelligence survival prediction model for gastric cancer based on clinicopathological features
Objective To construct a survival prediction model for gastric cancer(GC)with artificial intelligence(Al)algorithms and to verify its performance efficiency.Methods Clinicopathological information of 200 patients with gastric cancer treated in Lishui People's Hospital from June 2016 to May 2018 was analyzed,and the indicators highly correlated with survival outcome were screened out.Patients were divided into a training cohort(40 cases)and a testing cohort(160 cases)at a ratio of 2:8 by 10-fold cross-validation.GC survival prediction models were constructed based on pathological indexes in the training cohort by 6 Al algorithms,including stochastic gradient boosting(gbm),generalized linear model(glmnet),penalized logistic regression(plr),support vector machines with radial basis function Kernel(svmRadial),naive_bayes and random forest(ranger);and the prediction efficiency of models was verified in the testing cohort.Results Of the 200 patients,109 survived and 91 died.There were significant differences in tumor size,lymph node metastasis,tumor location,nerve invasion,and TNM stage between the survival group and the death group(all P＜0.05),and there indexes were correlated with the survival outcomes of patients(all P＜0.05).ROC curve showed that the AUC of the single index for predicting the survival of patients was all＞0.500.Comprehensive comparison of the multi-dimensional considerations of the six algorithms showed that the 5MP survival prediction model based on the svmRadial algorithm had the best comprehensive performance with an AUC of 0.817,sensitivity of 0.762,specificity of 0.833 and accuracy of 0.795.The AUC of the survival prediction model based on the combination of 5 clinicopathological features constructed with the svmRadial algorithm in the validation queue was 0.624.Conclusion The prediction model based on the combination of clinicopathological features and constructed by Al technology has excellent auxiliary potential in predicting the prognosis of gastric cancer patients.
万方数据
维普
