Improving Implementation of Lung Cancer Screening With Risk Prediction Models
Authors: Martin C. Tammemägi
Journal: Annals of Internal Medicine (2018)

Editorials3 July 2018Improving Implementation of Lung Cancer Screening With Risk Prediction ModelsMartin C. Tammemägi, DVM, MSc, PhDMartin C. Tammemägi, DVM, MSc, PhDBrock University, St. Catharines, Ontario, Canada (M.C.T.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M18-0986 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail In this issue, Katki and colleagues compare the performance of 9 lung cancer risk models in the National Institutes of Health–AARP Diet and Health Study and the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort (1). Each model used National Health Interview Survey data from 2010 to 2012 to estimate the number of U.S. persons eligible for screening. Four models (the Bach model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) demonstrated superior performance, selected similarly sized screening populations (7.6 ...References1. Katki HA, Kovalchik SA, Petito LC, Cheung LC, Jacobs E, Jemal A, et al. Implications of nine risk prediction models for selecting ever-smokers for computed tomography lung cancer screening. Ann Intern Med. 2018;169:10-9. doi:10.7326/M17-2701 LinkGoogle Scholar2. Tammemägi MC, Katki HA, Hocking WG, Church TR, Caporaso N, Kvale PA, et al. Selection criteria for lung-cancer screening. N Engl J Med. 2013;368:728-36. [PMID: 23425165] doi:10.1056/NEJMoa1211776 CrossrefMedlineGoogle Scholar3. Bach PB, Kattan MW, Thornquist MD, Kris MG, Tate RC, Barnett MJ, et al. Variations in lung cancer risk among smokers. J Natl Cancer Inst. 2003;95:470-8. [PMID: 12644540] CrossrefMedlineGoogle Scholar4. Ten Haaf K, Jeon J, Tammemägi MC, Han SS, Kong CY, Plevritis SK, et al. Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med. 2017;14:e1002277. [PMID: 28376113] doi:10.1371/journal.pmed.1002277 CrossrefMedlineGoogle Scholar5. Li K, Hüsing A, Sookthai D, Bergmann M, Boeing H, Becker N, et al. Selecting high-risk individuals for lung cancer screening: a prospective evaluation of existing risk models and eligibility criteria in the German EPIC cohort. Cancer Prev Res (Phila). 2015;8:777-85. [PMID: 26076698] doi:10.1158/1940-6207.CAPR-14-0424 CrossrefMedlineGoogle Scholar6. Weber M, Yap S, Goldsbury D, Manners D, Tammemägi M, Marshall H, et al. Identifying high risk individuals for targeted lung cancer screening: independent validation of the PLCOm2012 risk prediction tool. Int J Cancer. 2017;141:242-53. [PMID: 28249359] doi:10.1002/ijc.30673 CrossrefMedlineGoogle Scholar7. Tammemägi MC, Schmidt H, Martel S, McWilliams A, Goffin JR, Johnston MR, et al; PanCan Study Team. Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study. Lancet Oncol. 2017;18:1523-31. [PMID: 29055736] doi:10.1016/S1470-2045(17)30597-1 CrossrefMedlineGoogle Scholar8. Mazzone PJ, Silvestri GA, Patel S, Kanne JP, Kinsinger LS, Wiener RS, et al. Screening for lung cancer: CHEST Guideline and Expert Panel Report. Chest. 2018;153:954-85. [PMID: 29374513] doi:10.1016/j.chest.2018.01.016 CrossrefMedlineGoogle Scholar9. Kumar V, Cohen JT, van Klaveren D, Soeteman DI, Wong JB, Neumann PJ, et al. Risk-targeted lung cancer screening: a cost-effectiveness analysis. Ann Intern Med. 2018;168:161-9. [PMID: 29297005]. doi:10.7326/M17-1401 LinkGoogle Scholar10. Tammemägi MC, Church TR, Hocking WG, Silvestri GA, Kvale PA, Riley TL, et al. Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts. PLoS Med. 2014;11:e1001764. [PMID: 25460915] doi:10.1371/journal.pmed.1001764 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Brock University, St. Catharines, Ontario, Canada (M.C.T.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-0986.Corresponding Author: Martin C. Tammemägi, DVM, MSc, PhD, Department of Health Sciences, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, Ontario L2S 3A1, Canada; e-mail, martin.[email protected]ca.This article was published at Annals.org on 15 May 2018. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoImplications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening Hormuzd A. Katki , Stephanie A. Kovalchik , Lucia C. Petito , Li C. Cheung , Eric Jacobs , Ahmedin Jemal , Christine D. Berg , and Anil K. Chaturvedi Metrics Cited byStratification and management of patients ineligible for lung cancer screeningMilestones towards lung cancer screening implementationTargeted Incentive Programs For Lung Cancer Screening Can Improve Population Health And Economic Efficiency 3 July 2018Volume 169, Issue 1Page: 54-55KeywordsCohort studiesDisclosureHealth disparitiesHealth surveysLung and intrathoracic tumorsLung cancer screeningLungsQuality adjusted life yearsRisk assessmentsThorax ePublished: 15 May 2018 Issue Published: 3 July 2018 Copyright & PermissionsCopyright © 2018 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...