BACKGROUND: The efficacy of computed tomography (CT) screening for lung cancer remains controversial because results from the National Lung Screening Trial are not yet available. In this study, the authors used data from a single-arm CT screening trial to estimate the mortality reduction using a modeling-based approach to construct a control comparison arm. METHODS: To estimate the potential lung cancer mortality reduction because of CT screening, a previously developed and validated model was applied to the screening trial to predict the number of lung cancer deaths in the absence of screening. By using age, gender, and smoking characteristics matching those of the trial participants, the model was used to simulate 5000 trials in the absence of CT screening to produce the expected number of lung cancer deaths along with 95% confidence intervals (95% CIs), while adjusting for healthy volunteer bias. RESULTS: There were 64 observed lung cancer deaths in the screening cohort (n = 7995), whereas the model predicted 117.7 deaths (95% CI, 98 deaths-139 deaths), indicating a mortality reduction of 45.6% (P < .001). When a more conservative healthy volunteer adjustment was applied, 111.3 lung cancer deaths were predicted (95% CI, 91 deaths-132 deaths), for a lung cancer-specific mortality reduction of 42.5% (P < .001). CONCLUSIONS: The results of the current study indicate that CT screening along with early stage treatment can reduce lung cancer-specific mortality. This mortality reduction is greatly influenced by the protocol of nodule follow-up and treatment, and the length of follow-up.
BACKGROUND: The efficacy of computed tomography (CT) screening for lung cancer remains controversial because results from the National Lung Screening Trial are not yet available. In this study, the authors used data from a single-arm CT screening trial to estimate the mortality reduction using a modeling-based approach to construct a control comparison arm. METHODS: To estimate the potential lung cancer mortality reduction because of CT screening, a previously developed and validated model was applied to the screening trial to predict the number of lung cancer deaths in the absence of screening. By using age, gender, and smoking characteristics matching those of the trial participants, the model was used to simulate 5000 trials in the absence of CT screening to produce the expected number of lung cancer deaths along with 95% confidence intervals (95% CIs), while adjusting for healthy volunteer bias. RESULTS: There were 64 observed lung cancer deaths in the screening cohort (n = 7995), whereas the model predicted 117.7 deaths (95% CI, 98 deaths-139 deaths), indicating a mortality reduction of 45.6% (P < .001). When a more conservative healthy volunteer adjustment was applied, 111.3 lung cancer deaths were predicted (95% CI, 91 deaths-132 deaths), for a lung cancer-specific mortality reduction of 42.5% (P < .001). CONCLUSIONS: The results of the current study indicate that CT screening along with early stage treatment can reduce lung cancer-specific mortality. This mortality reduction is greatly influenced by the protocol of nodule follow-up and treatment, and the length of follow-up.
Authors: C I Henschke; D P Naidich; D F Yankelevitz; G McGuinness; D I McCauley; J P Smith; D Libby; M Pasmantier; M Vazquez; J Koizumi; D Flieder; N Altorki; O S Miettinen Journal: Cancer Date: 2001-07-01 Impact factor: 6.860
Authors: M R Spitz; X Wu; Y Wang; L E Wang; S Shete; C I Amos; Z Guo; L Lei; H Mohrenweiser; Q Wei Journal: Cancer Res Date: 2001-02-15 Impact factor: 12.701
Authors: Stephen J Swensen; James R Jett; Jeff A Sloan; David E Midthun; Thomas E Hartman; Anne-Marie Sykes; Gregory L Aughenbaugh; Frank E Zink; Shauna L Hillman; Gayle R Noetzel; Randolph S Marks; Amy C Clayton; Peter C Pairolero Journal: Am J Respir Crit Care Med Date: 2002-02-15 Impact factor: 21.405
Authors: Pamela M McMahon; Chung Yin Kong; Colleen Bouzan; Milton C Weinstein; Lauren E Cipriano; Angela C Tramontano; Bruce E Johnson; Jane C Weeks; G Scott Gazelle Journal: J Thorac Oncol Date: 2011-11 Impact factor: 15.609
Authors: Claudia I Henschke; Rowena Yip; Dorith Shaham; Javier J Zulueta; Samuel M Aguayo; Anthony P Reeves; Artit Jirapatnakul; Ricardo Avila; Drew Moghanaki; David F Yankelevitz Journal: J Thorac Imaging Date: 2021-01 Impact factor: 5.528