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Comparative Study
. 2021 Jun;124(12):2026-2034.
doi: 10.1038/s41416-021-01278-0. Epub 2021 Apr 12.

Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom

Hilary A Robbins  1 , Karine Alcala  2 , Anthony J Swerdlow  3 , Minouk J Schoemaker  3 , Nick Wareham  4 , Ruth C Travis  5 , Philip A J Crosbie  6 , Matthew Callister  7 , David R Baldwin  8 , Rebecca Landy  9 , Mattias Johansson  2
Affiliations
Comparative Study

Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom

Hilary A Robbins et al. Br J Cancer. 2021 Jun.

Erratum in

Abstract

Background: The 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.

Methods: We 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).

Results: Risk 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%).

Conclusion: In 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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Conflict of interest statement

H.A.R., K.A., A.J.S., M.J.S., N.W., R.C.T., M.C., R.L., M.J.: None. P.A.J.C.: consultancy and share options, Everest Detection. D.R.B.: reimbursement for participation on advisory boards, AstraZeneca, and MSD.

Figures

Fig. 1
Fig. 1. Calibration of lung cancer risk models in the UK Biobank, EPIC-UK, and Generations Study cohorts, as measured by the ratio of expected to observed cases.
UKB UK Biobank, GS Generations Study. Estimates for UK Biobank also appear in Table 2 and Supplementary Table 3.
Fig. 2
Fig. 2. Discrimination of lung cancer risk models in the UK Biobank, EPIC-UK, and Generations Study cohorts, as measured by the area under the ROC curve (AUC).
UKB UK Biobank, GS Generations Study. Estimates for UK Biobank also appear in Supplementary Tables 2 and 3.
See this image and copyright information in PMC

References

    1. Cancer Research UK. Cancer Statistics for the UK. https://www.cancerresearchuk.org/health-professional/cancer-statistics-f... (2020).
    1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018;68:394–424. - PubMed
    1. National Lung Screening Trial Research Team. Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 2011;365:395–409. doi: 10.1056/NEJMoa1102873. - DOI - PMC - PubMed
    1. de Koning, H. J., van der Aalst, C. M., de Jong, P. A., Scholten, E. T., Nackaerts, K., Heuvelmans, M. A. et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N. Engl. J. Med. 382, 503–513 (2020). - PubMed
    1. Moyer VA. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann. Intern. Med. 2014;160:330–338. - PubMed

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