Multi-strategy feature selection and multi-model machine learning for prognostic prediction in primary gastric...

Primary gastric diffuse large B-cell lymphoma (PG-DLBCL) exhibits heterogeneous outcomes, and conventional prognostic systems often fail to capture its unique clinicopathological features. We aimed to develop a robust, interpretable prognostic model to improve risk stratification and guide individualized management.

Data from 3773 PG-DLBCL patients (2000-2021) were extracted from the SEER database. Four complementary feature selection strategies-LASSO, Boruta, backward stepwise elimination, and best subset regression (BSR)-were employed to identify stable prognostic variables. Four machine learning (ML) algorithms (logistic regression, support vector machine, k-nearest neighbor, and XGBoost) were trained using these selected variables. Model performance was evaluated through discrimination (AUC), calibration, decision curve analysis, and internal validation. Shapley Additive Explanations (SHAP) were applied for interpretability, and patients were stratified into high- and low-risk groups.

Age, stage, chemotherapy, marital status, and income emerged as key prognostic determinants. The XGBoost model based on BSR-selected predictors achieved the highest performance with good calibration and net clinical benefit. SHAP analysis demonstrated that older age, advanced stage and absence of chemotherapy increased predicted risk, whereas marital status and higher income were protective. Risk stratification effectively distinguished survival outcomes in training and testing cohorts (p < 0.001). A web-based tool was developed for individualized risk assessment.

We established an interpretable, high-performing ML-based prognostic model for PG-DLBCL that integrates clinical, treatment, and socioeconomic factors. This tool enables precise risk stratification, supports individualized therapeutic decisions, and provides a methodological framework for prognostic modeling in this rare extranodal lymphomas.