Analyses of NLST data to validate Bach lung risk model and understand natural history of lung cancer to project maximal benefit
Principal Investigator
Name
Peter Bach
Degrees
MD, MAPP
Institution
Memorial Sloan-Kettering Cancer Center
Position Title
Director, Center for Health Policy and Outcomes
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-36
Initial CDAS Request Approval
Sep 12, 2013
Title
Analyses of NLST data to validate Bach lung risk model and understand natural history of lung cancer to project maximal benefit
Summary
This project has two components:
1: The NLST’s mortality results were achieved in a population at an elevated risk of lung cancer (current smokers, or former smokers who have quit within the last 15 years, are between the ages of 55-74 and have a smoking history of at least 35 py). However, since the publication NCI investigators have established that the potential benefit of screening varied significantly even within the NLST eligible population,(1) a finding predicted by a risk prediction model developed prior to the NLST release of its findings,(2) and now incorporated into an online risk assessment tool (http://nomograms.mskcc.org/Lung/Screening.aspx). The proposed project will utilize the NLST data to further evaluate (through ‘external validation’) this existing risk prediction model (the Bach lung cancer risk model), in order to assess its ability to identify those individuals who are most appropriate for LDCT screening.
2: It is plausible, but not yet tested, that the maximal benefit of annual lung cancer screening may be only 20 percent, or alternatively may be much greater if continued for longer periods of time. The former would be true if for instance the full benefit of screening decayed by the time of the next annual screen- a scenario that could hold true if lung cancers progressed very rapidly from detectable to incurable. Alternatively the full benefit might accumulate over years if the benefit of early detection takes years to become apparent because of long lead time in lung cancer natural history. This study will assess this hypothesis in two ways:
– an examination of the risk ratio of death from lung cancer in the CXR to CT arms in each year of the study in isolation and cumulatively. If the benefits are seen rapidly with a limited tail, the risk ratios should be relatively fixed over time. If they take a longer period to accumulate the risk ratio should rise.
– an examination of the patterns of lung cancers at first diagnosis among individuals who died of lung cancer in the study, and an examination of their CT findings prior to their diagnosis. If most patients who die of lung cancer went from having a negative CT to incurable lung cancer despite annual screening, this would suggest a short lead time.
References
1. Kovalchik SA, Tammemagi M, Berg CD, et al. Targeting of low-dose CT screening according to the risk of lung-cancer death. The New England journal of medicine 2013;369:245-54.
2. Bach PB, Kattan MW, Thornquist MD, et al. Variations in lung cancer risk among smokers. Journal of the National Cancer Institute 2003;95:470-8.
1: The NLST’s mortality results were achieved in a population at an elevated risk of lung cancer (current smokers, or former smokers who have quit within the last 15 years, are between the ages of 55-74 and have a smoking history of at least 35 py). However, since the publication NCI investigators have established that the potential benefit of screening varied significantly even within the NLST eligible population,(1) a finding predicted by a risk prediction model developed prior to the NLST release of its findings,(2) and now incorporated into an online risk assessment tool (http://nomograms.mskcc.org/Lung/Screening.aspx). The proposed project will utilize the NLST data to further evaluate (through ‘external validation’) this existing risk prediction model (the Bach lung cancer risk model), in order to assess its ability to identify those individuals who are most appropriate for LDCT screening.
2: It is plausible, but not yet tested, that the maximal benefit of annual lung cancer screening may be only 20 percent, or alternatively may be much greater if continued for longer periods of time. The former would be true if for instance the full benefit of screening decayed by the time of the next annual screen- a scenario that could hold true if lung cancers progressed very rapidly from detectable to incurable. Alternatively the full benefit might accumulate over years if the benefit of early detection takes years to become apparent because of long lead time in lung cancer natural history. This study will assess this hypothesis in two ways:
– an examination of the risk ratio of death from lung cancer in the CXR to CT arms in each year of the study in isolation and cumulatively. If the benefits are seen rapidly with a limited tail, the risk ratios should be relatively fixed over time. If they take a longer period to accumulate the risk ratio should rise.
– an examination of the patterns of lung cancers at first diagnosis among individuals who died of lung cancer in the study, and an examination of their CT findings prior to their diagnosis. If most patients who die of lung cancer went from having a negative CT to incurable lung cancer despite annual screening, this would suggest a short lead time.
References
1. Kovalchik SA, Tammemagi M, Berg CD, et al. Targeting of low-dose CT screening according to the risk of lung-cancer death. The New England journal of medicine 2013;369:245-54.
2. Bach PB, Kattan MW, Thornquist MD, et al. Variations in lung cancer risk among smokers. Journal of the National Cancer Institute 2003;95:470-8.
Aims
1: To externally validate the Bach lung cancer risk model using the NLST data.
2: To project the maximal benefit of LDCT screening through an analysis of the natural history of lung cancer using the risk ratio of death in the CXR to CT arms of the NLST as well as an examination of findings on the CT scans (both before and at diagnosis) of lung cancer decedents in the NLST.