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Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials
Author(s) -
Meza Rafael,
Haaf Kevin,
Kong Chung Yin,
Erdogan Ayca,
Black William C.,
Tammemagi Martin C.,
Choi Sung Eun,
Jeon Jihyoun,
Han Summer S.,
Munshi Vidit,
Rosmalen Joost,
Pinsky Paul,
McMahon Pamela M.,
Koning Harry J.,
Feuer Eric J.,
Hazelton William D.,
Plevritis Sylvia K.
Publication year - 2014
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/cncr.28623
Subject(s) - medicine , national lung screening trial , natural history , lung cancer , lung cancer screening , cancer screening , cancer , oncology
BACKGROUND The National Lung Screening Trial (NLST) demonstrated that low‐dose computed tomography screening is an effective way of reducing lung cancer (LC) mortality. However, optimal screening strategies have not been determined to date and it is uncertain whether lighter smokers than those examined in the NLST may also benefit from screening. To address these questions, it is necessary to first develop LC natural history models that can reproduce NLST outcomes and simulate screening programs at the population level. METHODS Five independent LC screening models were developed using common inputs and calibration targets derived from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Imputation of missing information regarding smoking, histology, and stage of disease for a small percentage of individuals and diagnosed LCs in both trials was performed. Models were calibrated to LC incidence, mortality, or both outcomes simultaneously. RESULTS Initially, all models were calibrated to the NLST and validated against PLCO. Models were found to validate well against individuals in PLCO who would have been eligible for the NLST. However, all models required further calibration to PLCO to adequately capture LC outcomes in PLCO never‐smokers and light smokers. Final versions of all models produced incidence and mortality outcomes in the presence and absence of screening that were consistent with both trials. CONCLUSIONS The authors developed 5 distinct LC screening simulation models based on the evidence in the NLST and PLCO. The results of their analyses demonstrated that the NLST and PLCO have produced consistent results. The resulting models can be important tools to generate additional evidence to determine the effectiveness of lung cancer screening strategies using low‐dose computed tomography. Cancer 2014;120:1713–1724 . © 2014 American Cancer Society .