Performance of various risk prediction models in a large lung cancer screening cohort in Gdańsk, Poland-a comparative...

Optimal selection criteria for the lung cancer screening programme remain a matter of an open debate. We performed a validation study of the three most promising lung cancer risk prediction models in a large lung cancer screening cohort of 6,631 individuals from a single European centre.

A total of 6,631 healthy volunteers (aged 50-79, smoking history ≥30 pack-years) were enrolled in the MOLTEST BIS programme between 2016 and 2018. Each participant underwent a low-dose computed chest tomography scan, and selected participants underwent a further diagnostic work-up. Various lung cancer prediction models were applied to the recruited screenees, i.e., (I) Tammemagi's Prostate, Colorectal, and Ovarian Cancer Screening Trial 2012 (PLCO_m2012), (II) Liverpool Lung Project (LLP) model, and (III) Bach's lung cancer risk model. Patients (I) with 6-year lung cancer probability ≥1.3% were considered as high risk in PLCO_m2012 model, (II) in LLP model with 5-year lung cancer probability ≥5.0%, and (III) in Bach's model with 5-year lung cancer probability ≥2.0%. The particular model cut-off values were employed to the cohort to evaluate each model's performance in the screened population.

Lung cancer was diagnosed in 154 (2.3%) participants. Based on the risk estimates by PLCO_m2012, LLP and Bach's models there were 82.4%, 50.3% and 19.8% of the MOLTEST BIS participants, respectively, who fulfilled the above-mentioned threshold criteria of a lung cancer development probability. Of those detected with lung cancer, 97.4%, 74.0% and 44.8% were eligible for screening by PLCO_m2012, LLP and Bach's model criteria, respectively. In Tammemagi's risk prediction model only four cases (2.6%) would have been missed from the group of 154 lung cancer patients primarily detected in the MOLTEST BIS.

Lung cancer screening enrollment based on the risk prediction models is superior to NCCN Group 1 selection criteria and offers a clinically significant reduction of screenees with a comparable proportion of detected lung cancer cases. Tammemagi's risk prediction model reduces the proportion of patients eligible for inclusion to a screening programme with a minimal loss of detected lung cancer cases.