ArticleTranslational lung cancer research2021
Risk-based prediction model for selecting eligible population for lung cancer screening among ever smokers in Korea.
Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
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16 citing papers in PubMed, 4 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Risk prediction for lung cancer screening: a systematic review and meta-regression.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- A systematic review and meta-analysis of lung cancer risk prediction models.Acta oncologica (Stockholm, Sweden) · 2025Pooled it
- Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Pooled it
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
- Understanding East-West differences in subsolid nodules: prevalence and overdiagnosis implications in lung cancer screening.Annals of medicine · 2025Article
- Structured narrative review on lung cancer screening: current evidence, clinical practice implications and implementation insights from a multidisciplinary task force and patient representatives.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study.Cancer medicine · 2025Article
- Head-to-head comparisons of risk discrimination by questionnaire-based lung cancer risk prediction models: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Lung Cancer Detection Using Bayesian Networks: A Retrospective Development and Validation Study on a Danish Population of High-Risk Individuals.Cancer medicine · 2025Article
- Optimal Pathways to Lung Cancer Screening in Primary Care Settings: A Scoping Review.Current oncology (Toronto, Ont.) · 2024Article
- Assessing the efficiency of eligibility criteria for low-dose computed tomography lung screening in China according to current guidelines.BMC medicine · 2024Article
- Impact of annual trend volume of low-dose computed tomography for lung cancer screening on overdiagnosis, overmanagement, and gender disparities.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Risk-based lung cancer screening in heavy smokers: a benefit-harm and cost-effectiveness modeling study.BMC medicine · 2024Article
- Lung cancer risk score for ever and never smokers in China.Cancer communications (London, England) · 2023Article
- Investigation on the incidence and risk factors of lung cancer among Chinese hospital employees.Thoracic cancer · 2022Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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%
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.
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