Description

Lung nodules are frequent findings on chest CT scans. The majority of nodules are benign, but a substantial fraction will be malignant. The decision to pursue invasive diagnostics currently rely on a risk estimate based on patient characteristics and radiological features of the nodule.

Accurate risk estimation is crucial as invasive diagnostics comes with a significant risk of severe complications such as collapsed lung, bleeding, infections and in rare cases death. The NIMBLE project seeks to improve the early risk assessment by incorporation of blood biomarkers in the risk algorithm. The study will include 1,200 patients with a nodule when patients are referred to the dept. of pulmonology and perform an addition blood draw. The blood will be analyzed using a wide range of blood tests. These will include standard blood tests, novel protein tumor markers specifically targeting lung cancer, an epigenetic smoking marker (AHRR methylation), and combined fragmentome, mutational and methylome analysis of cell-free tumor DNA.

Blood tests will be evaluated following REMARK guidelines in terms of lung cancer predictive performance but also in terms of feasibility in implementation in daily routine testing at a Dept. of Clinical Biochemistry.

The study will perform all tests on patient material collected at baseline when patients are referred to work-up and use machine learning to optimally combine blood tests with patient characteristics and radiological features.