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.

All patients:

• Age ≥ 50 years and < 80 years at the index date of diagnosis (for Cases) or pseudodiagnosis (for Controls).
• Presence of at least one extended blood analysis, spirometry and DLCO report within the 6 months before the index date.
• Chest CT scan performed in a non-urgent setting (electively) within the 6 months before the index date (= index CT).
• Active smokers at the index date or former smokers that ceased smoking within 15 years before the index date.
• Smoking history ≥ 20 pack-years.

Additional for Cases only: Confirmed histological diagnosis of bronchogenic lung cancer in the time period ≥ 2010 and ≤ 2020.

Additional for Controls only:

• Absence of lung cancer at all times ≤ 2020, confirmed by chest CT scan at the index date.
• Documented to live without diagnosis of lung cancer for at least 3 years after the index date.

Extended criteria for the lung cancer prediction subgroup:

In addition to the above stated inclusion criteria, patients in this subgroup have at least one extended blood analysis, spirometry and DLCO report available in the time interval between 3-5 years before the index date.

The study will include adult active or former smokers who are at high-risk of developing lung cancer, and would be considered suitable candidates for lung cancer screening. The study will focus on patients with confirmed bronchogenic lung cancer.