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dc.contributor.authorRobbins, HA
dc.contributor.authorAlcala, K
dc.contributor.authorSwerdlow, AJ
dc.contributor.authorSchoemaker, MJ
dc.contributor.authorWareham, N
dc.contributor.authorTravis, RC
dc.contributor.authorCrosbie, PAJ
dc.contributor.authorCallister, M
dc.contributor.authorBaldwin, DR
dc.contributor.authorLandy, R
dc.contributor.authorJohansson, M
dc.date.accessioned2021-06-11T12:58:28Z
dc.date.available2021-06-11T12:58:28Z
dc.date.issued2021-06-08
dc.identifier.citationBritish journal of cancer, 2021, 124 (12), pp. 2026 - 2034
dc.identifier.issn0007-0920
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4633
dc.identifier.eissn1532-1827
dc.identifier.doi10.1038/s41416-021-01278-0
dc.description.abstractBACKGROUND: 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.
dc.formatPrint-Electronic
dc.format.extent2026 - 2034
dc.languageeng
dc.language.isoeng
dc.publisherSPRINGERNATURE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleComparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom.
dc.typeJournal Article
dcterms.dateAccepted2021-01-13
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1038/s41416-021-01278-0
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBritish journal of cancer
pubs.issue12
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Aetiological Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Aetiological Epidemiology
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Aetiological Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Aetiological Epidemiology
pubs.publication-statusPublished
pubs.volume124
pubs.embargo.termsNot known
icr.researchteamAetiological Epidemiology
icr.researchteamAetiological Epidemiology
dc.contributor.icrauthorSchoemaker, Minouk


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