Machine learning-based predictive model for immune checkpoint inhibitors response in gastrointestinal cancers

Interpretable XGBoost models have shown accuracy, efficiency, and robustness in determining the association between input features and response rates. Among the input features, chemotherapy and tumor stage played the most important role in the prediction model. Due to the varying efficacy of ICIs in gastrointestinal cancers, personalized predictive models can greatly assist clinical decision-making. This model fills this gap in clinical practice and can provide more precise support for personalized treatment and risk avoidance.