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

PURPOSE

Low-dose computed tomography (LDCT) screening is effective in reducing lung cancer mortality by detecting the disease at earlier, more treatable stages. However, high false-positive rates and the associated risks of subsequent invasive diagnostic procedures present significant challenges. This study proposes an advanced pipeline that integrates machine learning (ML) and causal inference techniques to optimize lung cancer screening decisions.

MATERIALS AND METHODS

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. Explainable artificial intelligence techniques were applied to identify key risk factors, ensuring transparency and trust in the model's predictions. Causal ML methods were used to estimate individualized treatment effects of LDCT screening, answering the critical what-if question regarding risk reduction from LDCT.

RESULTS

We defined a high-risk cohort of 5,947 patients who underwent LDCT, along with matched controls, to evaluate the framework. Our models demonstrated predictive performance with AUCs of 0.777 and 0.793 for 1-year and 3-year risk predictions, respectively. Causal modeling showed a consistent reduction in lung cancer risk across different subgroups due to LDCT. Specifically, the doubly robust model showed an average risk reduction of 9.5% for males and 12% for females. Age-stratified results indicated a 9.5% reduction for individuals age 50-60 years, a 7.5% reduction for those age 60-70 years, and the largest reduction of 15.1% for the 70-80 age group.

CONCLUSION

Integrating ML and causal inference into clinical workflows offers a robust tool for enhancing lung cancer screening. This pipeline provides accurate risk assessments and actionable insights tailored to individuals, empowering clinicians and patients to make informed screening decisions. The differential risk reduction across subgroups highlights the importance of personalized screening in improving outcomes for populations at risk of lung cancer.