Construction and Validation of a Gastric Cancer Diagnostic Model Based on Blood Groups and Tumor Markers

The aim of this study is to explore the value of combined detection of ABO blood group and tumor markers in the diagnosis of gastric cancer. A total of 3650 gastric cancer patients treated in our center from January 2015 to December 2019, and 5822 controls were recruited, and divided into training set and validation set according to 7:3. The diagnostic and predictive model of gastric cancer was constructed by binary logistic regression method in the training set. The diagnostic value of the prediction model for gastric cancer was evaluated by calculating the prediction probability P value and drawing the Receiver operating characteristic (ROC) curve, and was verified in the validation set. The Area under the curve (AUC) of the diagnosis and prediction model in the training set was 0.936 (95%CI: 0.926-0.941), the sensitivity was 81.66%, and the specificity was 98.61%. In the validation set, the AUC was 0.941 (95%CI: 0.932-0.950), the sensitivity was 82.33%, and the specificity was 99.02%. Furthermore, the diagnostic model obtained in this study had a high diagnostic value for early gastric cancer patients in the healthy population (AUC of training set, validation set and total population were 0.906, 0.920 and 0.908, respectively). We constructed a diagnostic model for gastric cancer including blood group and tumor markers, which has high reference value for the diagnosis of gastric cancer patients, and the model can better distinguish early gastric cancer from healthy people.