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

ABSTRACT:

Background

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.

Methods

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, precis

SUBMITTER: Liu G 

PROVIDER: S-EPMC12559646 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature