Biomarker-based and interpretable machine learning framework for predicting pathological stage in gastric cancer: A...

Accurate preoperative staging of gastric cancer (GC) is essential for guiding treatment strategies. However, reliable noninvasive tools for distinguishing early-stage from advanced-stage GC remain limited.

This retrospective study enrolled 434 patients with GC. Eleven supervised machine learning algorithms were developed using preoperative laboratory parameters and engineered ratio features capturing inflammatory, metabolic, and tumor-related profiles. CatBoost showed superior performance and was selected for SHapley Additive exPlanations (SHAP)-based interpretation. A forward feature selection strategy identified an optimal nine-feature panel. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score, with robustness validated through repeated 10-fold cross-validation and 1000 bootstrap iterations.

Among 434 patients, 251 (57.8%) had stage I and 183 (42.2%) had stages II-III disease. Incorporating biologically informed ratio features significantly enhanced model performance; CatBoost's AUC improved from 0.802 to 0.981. SHAP-based selection yielded a compact, interpretable nine-feature model. The final CatBoost model achieved a mean AUC of 0.9499 (95% confidence interval (CI): 0.9421-0.9570), with high consistency across cross-validation folds. SHAP analysis identified uric acid (UA) and APTT as key predictors, and interaction analysis revealed stable multivariate relationships, supporting the model's biological plausibility.

We developed a robust, interpretable machine learning model for GC staging using routine blood tests and derived ratio features. The model demonstrated excellent discrimination, interpretability, and clinical utility, offering a practical tool for personalized risk stratification and treatment planning.