title: The impact of overdiagnosis on the selection of efficient lung cancer screening strategies.
abstract: The U.S. Preventive Services Task Force (USPSTF) recently updated their national lung screening guidelines and recommended low-dose computed tomography (LDCT) for lung cancer (LC) screening through age 80. However, the risk of overdiagnosis among older populations is a concern. Using four comparative models from the Cancer Intervention and Surveillance Modeling Network, we evaluate the overdiagnosis of the screening program recommended by USPSTF in the U.S. 1950 birth cohort. We estimate the number of LC deaths averted by screening (D) per overdiagnosed case (O), yielding the ratio D/O, to quantify the trade-off between the harms and benefits of LDCT. We analyze 576 hypothetical screening strategies that vary by age, smoking, and screening frequency and evaluate efficient screening strategies that maximize the D/O ratio and other metrics including D and life-years gained (LYG) per overdiagnosed case. The estimated D/O ratio for the USPSTF screening program is 2.85 (model range: 1.5-4.5) in the 1950 birth cohort, implying LDCT can prevent ∼3 LC deaths per overdiagnosed case. This D/O ratio increases by 22% when the program stops screening at an earlier age 75 instead of 80. Efficiency frontier analysis shows that while the most efficient screening strategies that maximize the mortality reduction (D) irrespective of overdiagnosis screen through age 80, screening strategies that stop at age 75 versus 80 produce greater efficiency in increasing life-years gained per overdiagnosed case. Given the risk of overdiagnosis with LC screening, the stopping age of screening merits further consideration when balancing benefits and harms.
authors.affiliation: Department of Medicine, Stanford University, Palo Alto, CA.|Department of Radiology, Stanford University, Palo Alto, CA.
authors.forename: Summer S
authors.initials: SS
authors.lastname: Han
authors.affiliation: Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: Kevin
authors.initials: K
authors.lastname: Ten Haaf
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA.
authors.forename: William D
authors.initials: WD
authors.lastname: Hazelton
authors.affiliation: Department of Radiology, Massachusetts General Hospital, Boston, MA.
authors.forename: Vidit N
authors.initials: VN
authors.lastname: Munshi
authors.affiliation: Department of Epidemiology, University of Michigan, Ann Arbor, MI.
authors.forename: Jihyoun
authors.initials: J
authors.lastname: Jeon
authors.affiliation: Department of Medicine, Stanford University, Palo Alto, CA.
authors.forename: Saadet A
authors.initials: SA
authors.lastname: Erdogan
authors.affiliation: Department of Radiology, Massachusetts General Hospital, Boston, MA.
authors.forename: Colden
authors.initials: C
authors.lastname: Johanson
authors.affiliation: Department of Radiology, Massachusetts General Hospital, Boston, MA.
authors.forename: Pamela M
authors.initials: PM
authors.lastname: McMahon
authors.affiliation: Department of Epidemiology, University of Michigan, Ann Arbor, MI.
authors.forename: Rafael
authors.initials: R
authors.lastname: Meza
authors.affiliation: Department of Radiology, Massachusetts General Hospital, Boston, MA.
authors.forename: Chung Yin
authors.initials: CY
authors.lastname: Kong
authors.affiliation: Division of Cancer Prevention, National Cancer Institute, Bethesda, MD.
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Feuer
authors.affiliation: Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: Harry J
authors.initials: HJ
authors.lastname: de Koning
authors.affiliation: Department of Radiology, Stanford University, Palo Alto, CA.
authors.forename: Sylvia K
authors.initials: SK
authors.lastname: Plevritis
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008403:Mass Screening / Q000379:methods; D000069342:Medical Overuse / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods; D013997:Time Factors; D014057:Tomography, X-Ray Computed
keywords: USPSTF; computed tomography; health policy; lung cancer; lung cancer screening; microsimulation; overdiagnosis; simulation model