ArticleBritish journal of cancer2021
Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom.
Article in British journal of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 41 papers, 2 of them syntheses that pooled it.
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41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- A systematic review and meta-analysis of lung cancer risk prediction models.Acta oncologica (Stockholm, Sweden) · 2025Pooled it
- [China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2023 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Guideline
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
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- Blood-based DNA methylation marker model for short-term and long-term lung cancer risk prediction.BMC medicine · 2026Article
- From evidence to action: the IARC's role in strengthening cancer prevention and early detection.Journal of the National Cancer Institute. Monographs · 2026Review
- Next-generation proteomics improves lung cancer risk prediction.Molecular oncology · 2026Article
- Article
- Dynamic screening initiation using 16 plasma protein biomarkers with polygenic risk and PLCOm2012: a precision prevention framework for lung cancer.Journal of translational medicine · 2025Article
- DNA methylation-based clocks, tobacco smoking, and lung cancer risk.BMC medicine · 2025Article
- Risk-Adapted Lung Cancer Screening Starting Ages for Former Smokers.JAMA network open · 2025Article
- Impact of Low-Dose Computed Tomography Findings on Cigarette Smoking Cessation Among High-Risk Adults Participating in Lung Cancer Screening.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025Observational
- Article
- Assessment and recalibration of seventeen lung cancer risk prediction models in approximately one million Chinese population utilising healthcare big data: a retrospective cohort analysis.The Lancet regional health. Western Pacific · 2025Article
- Association of circulating metabolic biomarkers with risk of lung cancer: a population-based prospective cohort study.BMC medicine · 2025Article
- Cancer incidence inconsistency between UK Biobank participants and the population: a prospective cohort study.BMC 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
- Accelerated biological aging as potential mediator in the relationship between central obesity and lung cancer risk.Frontiers in aging · 2025Article
- Pulmonologists-level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach.Scientific reports · 2024Article
- Pitfalls in interpreting calibration in comparative evaluations of risk models for precision lung cancer screening.NPJ precision oncology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 8 institutions in 3 countries.
Funding
Abstract
backgroundThe National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK.
methodsWe analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC).
resultsRisk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%).
conclusionIn UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.
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