Risk-based prediction model for selecting eligible population for lung cancer screening among ever smokers in Korea.

Content Provider	Europe PMC
Author	Park, Boyoung Kim, Yeol Lee, Jaeho Lee, Nayoung Jang, Seung Hun
Copyright Year	2021
Abstract	BackgroundThis study developed a new lung cancer risk prediction model for the Korean population and evaluated the performance, compared to the previously reported risk models developed in Western countries.MethodsAmong the 6,811,893 people who received health examinations from the Korean National Health Insurance Service, 969,351 ever-smokers (40–79 years) were included. Performance of Bach, Lung Cancer Risk Models for Screening, PLCOM2012, Pittsburgh, and Liverpool Lung Project models were evaluated. The ever-smokers were divided into the training and validation datasets by random sampling. The lung cancer risk model was developed and validated in the Korean population. The efficiency of model-based selection for lung cancer screening was compared with the eligible criteria of the National Lung Screening Trial (NLST).ResultsThe Korean lung cancer risk model showed the area under the curve and expected/observed (E/O) ratio of 0.816 and 0.983 in the training dataset and 0.816 and 0.988 in the validation dataset. The Korean lung cancer risk model included age-mean of age, square of age-mean of age, sex, square root of pack-years of smoking, years since cessation, physical activity, alcohol consumption, body mass index, and medical history of chronic pulmonary obstructive disease, emphysema, pneumoconiosis, and interstitial pulmonary disease. Compared with the NLST criteria, the Korean lung cancer risk model’s cut-off criteria (>2.1%) had more improved sensitivity (61.4% vs. 44.3%) and positive predictive value (4.1% vs. 2.9%). The Korean lung cancer risk model showed better discrimination and calibration than previously developed models in Western population.ConclusionsThe Korean lung cancer risk model can select eligible population for low-dose computed tomography screening among the Asian population. The efficiency of risk model-based selection for lung cancer screening is superior to that of fixed criteria-based selection.
ISSN	22186751
Journal	Translational Lung Cancer Research
Volume Number	10
PubMed Central reference number	PMC8743515
Issue Number	12
PubMed reference number	35070749
e-ISSN	22264477
DOI	10.21037/tlcr-21-566
Language	English
Publisher	AME Publishing Company
Publisher Date	2021-12-01
Access Restriction	Open
Rights License	Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0. 2021 Translational Lung Cancer Research. All rights reserved.
Subject Keyword	Lung cancer low-dose computed tomography screening (LDCT screening) National Lung Screening Trial criteria (NLST criteria) prediction model
Content Type	Text
Resource Type	Article
Subject	Oncology