alexa Evaluating Long-Term Outcomes via Computed Tomography in Lung Cancer Screening | OMICS International | Abstract
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
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Research Article

Evaluating Long-Term Outcomes via Computed Tomography in Lung Cancer Screening

Dongfeng Wu1*, Ruiqi Liu1, Beth Levitt2, Tom Riley2and Kathy B. Baumgartner3

1Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, USA

2Information Management Services, Rockville, MD 20852, USA

3Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, USA

Corresponding Author:
Dongfeng Wu
Department of Bioinformatics and Biostatistics
School of Public Health and Information Sciences
University of Louisville, Louisville, KY 40202, USA
Tel: 1-502-852-1888
Fax: 1-502-852-3291
E-mail: [email protected]

Received Date: June 20, 2016; Accepted Date: June 23, 2016; Published Date: June 30, 2016

Citation: Wu D, Liu R, Levitt B, Riley T, Baumgartner KB (2016) Evaluating Long-Term Outcomes via Computed Tomography in Lung Cancer Screening. J Biom Biostat 7:313. doi:10.4172/2155-6180.1000313

Copyright: © 2016 Dongfeng Wu, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Objectives: Future outcomes of computed tomography in lung cancer screening were evaluated using recently derived probability formula in the disease progressive model, and the recently completed National Lung Screening Trial computed tomography (NLST-CT) data.
Methods: Every participant in a screening program would fall into one of the four disjoint groups eventually: symptom-free-life, no-early-detection, true-early-detection and overdiagnosis, depending on whether he/she would be diagnosed with cancer and whether symptoms would have appeared before death. The probability of each outcome was a function of an individual’s current age, past and future screening frequency and the three key parameters: screening sensitivity, sojourn time and time in the disease-free state. The predictive probability was estimated for people with and without screening histories. Percentage of over-diagnosis among the screen-detected cases was also presented with human lifetime as a random variable.
Results: The probability of heavy smokers to live a lung-cancer-free life would depend on their current age; it was about 80%, 86% and 94% for the 60, 70, and 80 years old respectively. The probabilities of no-early detection and true-early-detection were determined by the future screening interval and the current age: the probability of no-earlydetection would increase to about three times if the future screening interval changes from annual to biennial; while the probability of true-early-detection would decrease to about 75% if the future screening interval changes from annual to biennial. The probability of over-diagnosis among the screen-detected was increasing as people aging: ~3%, 5% and 9% for the 60, 70, and 80 years old correspondingly; this probability decreases slightly when the historic screening interval increases.
Conclusion: This research provided the estimated probabilities of the four outcomes in the future and the percentage of overdiagnosis among the screen-detected cases. It provided a practical approach on the evaluation of long-term outcomes via CT in lung cancer screening.

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