A practical recurrence risk model based on Lasso-Cox regression for gastric cancer

Gastric cancer remains huge cancer threat worldwide. Detecting the recurrence of gastric cancer after treatment is especially important in improving the prognosis of patients. We aim to fit different risk models with different clinical variables for patients with gastric cancer, which further provides applicable guidance to clinical doctors for their patients.

We collected the primary data from the medical record system in Lanzhou University Second Hospital and further cleaned the primary data via assessing data integrity artificially; meanwhile, detailed conclusion criteria and exclusion criteria were made. We used R software (version 4.1.3) and SPSS 25.0 to analyze data and build models, in which SPSS was used to analyze the correlation and difference of different items in the training set and testing set, and different R packages were used to run LASSO regression, Cox regression and nomogram for variable selection, model construction and model validation.

A total of 649 patients were included in our data analysis and model building. In LASSO regression selection, seven variables, pathological stage, tumor size, the number of total lymph nodes, the number of metastatic lymph nodes, intraoperative blood loss (IBL), the level of AFP and CA199, showed their correlation to the dependent variable. The multivariable Cox regression model fitted using these seven variables showed medium prediction ability, with an AUC of 0.840 in the training set and 0.756 in the testing set.

Pathological stage, tumor size, the number of total lymph nodes, the number of metastatic lymph nodes, IBL, the level of AFP and CA199 are significant in identifying recurrence risk for gastric cancer patients after radical gastrectomy.