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

Marcin Ostrowski; Franciszek Bińczyk; Tomasz Marjański; Robert Dziedzic; Sylwia Pisiak; Sylwia Małgorzewicz; Mariusz Adamek; Joanna Polańska; Witold Rzyman

doi:10.21037/tlcr-20-753

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

Transl Lung Cancer Res. 2021 Feb;10(2):1083-1090. doi: 10.21037/tlcr-20-753.

Authors

Marcin Ostrowski¹, Franciszek Bińczyk², Tomasz Marjański¹, Robert Dziedzic¹, Sylwia Pisiak³, Sylwia Małgorzewicz⁴, Mariusz Adamek⁵, Joanna Polańska², Witold Rzyman¹

Affiliations

¹ Department of Thoracic Surgery, Medical University of Gdańsk, Gdańsk, Poland.
² Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.
³ Department of Non-Invasive Cardiac Diagnostics, Medical University of Gdańsk, Gdańsk, Poland.
⁴ Department of Clinical Nutrition and Dietetics, Medical University of Gdańsk, Gdańsk, Poland.
⁵ Department of Thoracic Surgery, Medical University of Silesia, Katowice, Poland.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Lung cancer; low-dose computed tomography; risk prediction models; screening.