This observational, cross-sectional study in lung cancer patients and lung cancer-free controls aims to develop a machine learning model for early detection of LC based on routine, widely accessible and minimally invasive clinical investigations. The model with adequate predictive performance could later be used in clinical practice as an aid in defining the optimal population and timing for lung cancer screening program.
Study Type
OBSERVATIONAL
Enrollment
7,500
No interventions.
University Clinic of Respiratory and Allergic Diseases Golnik
Golnik, Slovenia
Jozef Stefan Institute
Ljubljana, Slovenia
Develop a model with high predictive performance for early detection of non-small cell lung cancer (NSCLC) in the eligible patient population.
The primary outcome is tested by calculating a joint rectangular 95% confidence region for {sensitivity, specificity} and compared with the reported accuracy of NLST study screening criteria.
Time frame: 11 years
Demonstrate that the newly developed model achieves higher prediction accuracy than the well-validated model PLCOm2012.
Time frame: 11 years
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