Optimizing Strategy for Lung Cancer Screening: From Risk Prediction to Clinical Decision Support

This study proposes an advanced pipeline that integrates machine learning (ML) and causal inference techniques to optimize lung cancer screening decisions. Using real-world data from the OneFlorida+ Clinical Research Consortium, we developed ML models to predict individual lung cancer risk and estimate the benefits of LDCT screening, and applied explainable artificial intelligence techniques to identify key risk factors. The models demonstrated predictive performance with AUCs of 0.777 and 0.793 for 1-year and 3-year risk predictions, respectively.