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Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling
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-specifi...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4076275/ https://www.ncbi.nlm.nih.gov/pubmed/24979231 http://dx.doi.org/10.1371/journal.pone.0099978 |
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author | McMahon, Pamela M. Meza, Rafael Plevritis, Sylvia K. Black, William C. Tammemagi, C. Martin Erdogan, Ayca ten Haaf, Kevin Hazelton, William Holford, Theodore R. Jeon, Jihyoun Clarke, Lauren Kong, Chung Yin Choi, Sung Eun Munshi, Vidit N. Han, Summer S. van Rosmalen, Joost Pinsky, Paul F. Moolgavkar, Suresh de Koning, Harry J. Feuer, Eric J. |
author_facet | McMahon, Pamela M. Meza, Rafael Plevritis, Sylvia K. Black, William C. Tammemagi, C. Martin Erdogan, Ayca ten Haaf, Kevin Hazelton, William Holford, Theodore R. Jeon, Jihyoun Clarke, Lauren Kong, Chung Yin Choi, Sung Eun Munshi, Vidit N. Han, Summer S. van Rosmalen, Joost Pinsky, Paul F. Moolgavkar, Suresh de Koning, Harry J. Feuer, Eric J. |
author_sort | McMahon, Pamela M. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4076275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-40762752014-07-02 Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling McMahon, Pamela M. Meza, Rafael Plevritis, Sylvia K. Black, William C. Tammemagi, C. Martin Erdogan, Ayca ten Haaf, Kevin Hazelton, William Holford, Theodore R. Jeon, Jihyoun Clarke, Lauren Kong, Chung Yin Choi, Sung Eun Munshi, Vidit N. Han, Summer S. van Rosmalen, Joost Pinsky, Paul F. Moolgavkar, Suresh de Koning, Harry J. Feuer, Eric J. PLoS One Research Article 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. Public Library of Science 2014-06-30 /pmc/articles/PMC4076275/ /pubmed/24979231 http://dx.doi.org/10.1371/journal.pone.0099978 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article McMahon, Pamela M. Meza, Rafael Plevritis, Sylvia K. Black, William C. Tammemagi, C. Martin Erdogan, Ayca ten Haaf, Kevin Hazelton, William Holford, Theodore R. Jeon, Jihyoun Clarke, Lauren Kong, Chung Yin Choi, Sung Eun Munshi, Vidit N. Han, Summer S. van Rosmalen, Joost Pinsky, Paul F. Moolgavkar, Suresh de Koning, Harry J. Feuer, Eric J. Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling |
title | Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling |
title_full | Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling |
title_fullStr | Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling |
title_full_unstemmed | Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling |
title_short | Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling |
title_sort | comparing benefits from many possible computed tomography lung cancer screening programs: extrapolating from the national lung screening trial using comparative modeling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4076275/ https://www.ncbi.nlm.nih.gov/pubmed/24979231 http://dx.doi.org/10.1371/journal.pone.0099978 |
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