Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom
AuthorsRobbins H A
Swerdlow A J
Schoemaker M J
Travis R C
Crosbie Philip A J
Baldwin D R
AffiliationInternational Agency for Research on Cancer, Lyon, France.
MetadataShow full item record
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.
CitationRobbins HA, Alcala K, Swerdlow AJ, Schoemaker MJ, Wareham N, Travis RC, et al. Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom. Br J Cancer. 2021 Apr 12;124(12):2026–34.
JournalBritish Journal of Cancer
- Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening.
- Authors: Katki HA, Kovalchik SA, Petito LC, Cheung LC, Jacobs E, Jemal A, Berg CD, Chaturvedi AK
- Issue date: 2018 Jul 3
- Analysis of lung cancer risk model (PLCO<sub>M2012</sub> and LLP<sub>v2</sub>) performance in a community-based lung cancer screening programme.
- Authors: Lebrett MB, Balata H, Evison M, Colligan D, Duerden R, Elton P, Greaves M, Howells J, Irion K, Karunaratne D, Lyons J, Mellor S, Myerscough A, Newton T, Sharman A, Smith E, Taylor B, Taylor S, Walsham A, Whittaker J, Barber PV, Tonge J, Robbins HA, Booton R, Crosbie PAJ
- Issue date: 2020 Aug
- Liverpool Lung Project lung cancer risk stratification model: calibration and prospective validation.
- Authors: Field JK, Vulkan D, Davies MPA, Duffy SW, Gabe R
- Issue date: 2021 Feb
- A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies.
- Authors: Ten Haaf K, Bastani M, Cao P, Jeon J, Toumazis I, Han SS, Plevritis SK, Blom EF, Kong CY, Tammemägi MC, Feuer EJ, Meza R, de Koning HJ
- Issue date: 2020 May 1
- Risk prediction models versus simplified selection criteria to determine eligibility for lung cancer screening: an analysis of German federal-wide survey and incidence data.
- Authors: Hüsing A, Kaaks R
- Issue date: 2020 Oct