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

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

RESULTS

Lung cancer was diagnosed in 154 (2.3%) participants. Based on the risk estimates by PLCO

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.

Informations de copyright

2021 Translational Lung Cancer Research. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tlcr-20-753). The series “Implementation of CT-based screening of lung cancer” was commissioned by the editorial office without any funding or sponsorship. WR served as the unpaid Guest Editor of the series. TM reports personal fees from Roche/Genentech, outside the submitted work. The authors have no other conflicts of interest to declare.