Individualized-risk based computed tomography lung cancer screening strategies: an evaluation using comparative microsimulation modeling
Principal Investigator
Name
Kevin ten Haaf
Degrees
MSc
Institution
Erasmus MC
Position Title
Junior researcher
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-83
Initial CDAS Request Approval
May 2, 2014
Title
Individualized-risk based computed tomography lung cancer screening strategies: an evaluation using comparative microsimulation modeling
Summary
The United States Preventive Services Task Force (USPSTF) recommends annual screening to smokers aged 55 through 80, who smoked at least 30 pack-years and quit less than 15 years ago, based on Cancer Intervention and Surveillance Modeling Network (CISNET) Lung Model extrapolations which investigated eligibility criteria based on age, pack-years and years since cessation. 1 2
However, recent publications argue that eligibility based on individualized risk could lead to more effective screening programs compared to the National Lung Screening Trial (NLST) criteria, which were based on age, pack-years and years since cessation.3 4 Investigating the long term harms and benefits of screening programs with eligibility based on individualized risk is therefore of great interest.
In this project, the CISNET-Lung Group will investigate screening programs based on individualized risk. Lung cancer screening strategies based on several individualized lung cancer (death) risk models, such as the PLCOm2012 model, will be investigated.4
We will use data from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) to determine suitable risk thresholds for screening eligibility for each individual risk model. These models and risk thresholds will be applied to individuals from a 1950 U.S. cohort, to determine their screening eligibility for each risk model and risk threshold.
The individualized risk-based programs will then be investigated using microsimulation modeling. This allows us to investigate the future harms and benefits of implementing these programs, such as the number of lung cancer deaths averted, the number of life-years gained and the number of overdiagnosed cases.
References:
1. Moyer VA. Screening for Lung Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med 2013.
2. de Koning HJ, Meza R, Plevritis SK, et al. Benefits and Harms of Computed Tomography Lung Cancer Screening Strategies: A Comparative Modeling Study for the U.S. Preventive Services Task Force Ann Intern Med 2013; [published online ahead of print December 31, 2013], doi: 10.7326/M13-2316.
3. Aberle DR, Adams AM, Berg CD, et al. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. N Engl J Med 2011;365(5):395-409.
4. Tammemägi MC, Katki HA, Hocking WG, et al. Selection Criteria for Lung-Cancer Screening. N Engl J Med 2013;368(8):728-36.
However, recent publications argue that eligibility based on individualized risk could lead to more effective screening programs compared to the National Lung Screening Trial (NLST) criteria, which were based on age, pack-years and years since cessation.3 4 Investigating the long term harms and benefits of screening programs with eligibility based on individualized risk is therefore of great interest.
In this project, the CISNET-Lung Group will investigate screening programs based on individualized risk. Lung cancer screening strategies based on several individualized lung cancer (death) risk models, such as the PLCOm2012 model, will be investigated.4
We will use data from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) to determine suitable risk thresholds for screening eligibility for each individual risk model. These models and risk thresholds will be applied to individuals from a 1950 U.S. cohort, to determine their screening eligibility for each risk model and risk threshold.
The individualized risk-based programs will then be investigated using microsimulation modeling. This allows us to investigate the future harms and benefits of implementing these programs, such as the number of lung cancer deaths averted, the number of life-years gained and the number of overdiagnosed cases.
References:
1. Moyer VA. Screening for Lung Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med 2013.
2. de Koning HJ, Meza R, Plevritis SK, et al. Benefits and Harms of Computed Tomography Lung Cancer Screening Strategies: A Comparative Modeling Study for the U.S. Preventive Services Task Force Ann Intern Med 2013; [published online ahead of print December 31, 2013], doi: 10.7326/M13-2316.
3. Aberle DR, Adams AM, Berg CD, et al. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. N Engl J Med 2011;365(5):395-409.
4. Tammemägi MC, Katki HA, Hocking WG, et al. Selection Criteria for Lung-Cancer Screening. N Engl J Med 2013;368(8):728-36.
Aims
To investigate the long term effects of implementing screening programs based on individualized risk and how they compare to the currently recommended screening program recommended by the USPSTF.
Collaborators
Harry J. de Koning (Erasmus Medical Center)
Suresh H. Moolgavkar (Fred Hutchinson Cancer Research Center)
Jihyoun Jeon (Fred Hutchinson Cancer Research Center)
Chung Yin Kong (Massachussets General Hospital)
Vidit Munshi (Massachussets General Hospital)
Summer S. Han (Stanford University)
Sylvia K. Plevritis (Stanford University)
Rafael Meza (University of Michigan)
Eric J. Feuer (National Cancer Institute)
Martin C. Tammemägi (Brock University)
Related Publications
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A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies.
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
J Natl Cancer Inst. 2020 May 1; Volume 112 (Issue 5): Pages 466-479 PUBMED -
Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study.
Ten Haaf K, Jeon J, Tammemägi MC, Han SS, Kong CY, Plevritis SK, Feuer EJ, de Koning HJ, Steyerberg EW, Meza R
PLoS Med. 2017 Apr; Volume 14 (Issue 4): Pages e1002277 PUBMED -
Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials.
Meza R, ten Haaf K, Kong CY, Erdogan A, Black WC, Tammemagi MC, Choi SE, Jeon J, Han SS, Munshi V, van Rosmalen J, Pinsky P, McMahon PM, de Koning HJ, Feuer EJ, Hazelton WD, Plevritis SK
Cancer. 2014 Jun; Volume 120 (Issue 11): Pages 1713-24 PUBMED