Bridging artificial intelligence and clinical decision-making in gastric cancer surgery

Table 1 Overview of machine learning approaches used in prognostic modeling in gastric cancer surgery

Method	Application	Strengths	Limitations
Random forest	Survival prediction, complication risk	Handles nonlinear data, robust	Limited interpretability
Support vector machine	Classification of outcomes	High accuracy in small datasets	Sensitive to parameter tuning
Gradient boosting (XGBoost)	Risk prediction models	High predictive performance	Risk of overfitting
Deep learning (CNN)	Imaging analysis (CT, histopathology)	Detects complex patterns	Black-box nature
Radiomics models	Tumor heterogeneity analysis	Integrates imaging features	Reproducibility challenges

XGBoost: Extreme gradient boosting; CNN: Convolutional neural network; CT: Computed tomography.