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About this Publication
Title
Comparing benefits from many possible computed tomography lung cancer screening programs: extrapolating from the National Lung Screening Trial using comparative modeling.
Pubmed ID
24979231 (View this publication on the PubMed website)
Publication
PLoS ONE. 2014; Volume 9 (Issue 6): Pages e99978
Authors
McMahon PM, Meza R, Plevritis SK, Black WC, Tammemagi CM, Erdogan A, ten Haaf K, Hazelton W, Holford TR, Jeon J, Clarke L, Kong CY, Choi SE, Munshi VN, Han SS, van Rosmalen J, Pinsky PF, Moolgavkar S, de Koning HJ, Feuer EJ
Affiliations
  • Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America; Department of Radiology, Harvard Medical School, Boston, Massachusetts, United States of America.
  • Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America.
  • Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Department of Radiology, Dartmouth Medical School, Hanover, New Hampshire, United States of America.
  • Department of Community Health Sciences, Brock University, Ontario, Canada.
  • Department of Public Health, Erasmus MC, Rotterdam, Netherlands.
  • Program of Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
  • Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.
  • Department of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
  • Cornerstone Systems Northwest, Inc., Lynden, Washington, United States of America.
...show more
  • Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, United States of America.
  • Department of Epidemiology, School of Public Health University of Washington, Seattle, Washington, United States of America, and Department of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
  • Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, United States of America.
Abstract

BACKGROUND: The National Lung Screening Trial (NLST) demonstrated that in current and former smokers aged 55 to 74 years, with at least 30 pack-years of cigarette smoking history and who had quit smoking no more than 15 years ago, 3 annual computed tomography (CT) screens reduced lung cancer-specific mortality by 20% relative to 3 annual chest X-ray screens. We compared the benefits achievable with 576 lung cancer screening programs that varied CT screen number and frequency, ages of screening, and eligibility based on smoking.

METHODS AND FINDINGS: We used five independent microsimulation models with lung cancer natural history parameters previously calibrated to the NLST to simulate life histories of the US cohort born in 1950 under all 576 programs. 'Efficient' (within model) programs prevented the greatest number of lung cancer deaths, compared to no screening, for a given number of CT screens. Among 120 'consensus efficient' (identified as efficient across models) programs, the average starting age was 55 years, the stopping age was 80 or 85 years, the average minimum pack-years was 27, and the maximum years since quitting was 20. Among consensus efficient programs, 11% to 40% of the cohort was screened, and 153 to 846 lung cancer deaths were averted per 100,000 people. In all models, annual screening based on age and smoking eligibility in NLST was not efficient; continuing screening to age 80 or 85 years was more efficient.

CONCLUSIONS: Consensus results from five models identified a set of efficient screening programs that include annual CT lung cancer screening using criteria like NLST eligibility but extended to older ages. Guidelines for screening should also consider harms of screening and individual patient characteristics.

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