Literature DB >> 3567302

The natural history of lung cancer in a periodically screened population.

B J Flehinger, M Kimmel.   

Abstract

A mathematical model of the progression kinetics of lung cancer in a periodically screened population is proposed and data collected by the Memorial Sloan-Kettering Cancer Center in New York are used for parameter estimation. It is assumed that the development of adenocarcinoma of lung is a stochastic process with two stages, early and advanced, characterized by mean times, detection probabilities, and cure probabilities. Confidence regions of these parameters are estimated using a number of novel techniques. It is found, surprisingly, that the mean duration of the early stage is at least 4 years, the detectability less than .2, and the curability less than .5. These estimates imply that annual radiographic screening from age 45 to 80 might decrease mortality from adenocarcinoma of lung by something less than 20%.

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Year:  1987        PMID: 3567302

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  8 in total

Review 1.  Calibration methods used in cancer simulation models and suggested reporting guidelines.

Authors:  Natasha K Stout; Amy B Knudsen; Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

Review 2.  Dynamic microsimulation models for health outcomes: a review.

Authors:  Carolyn M Rutter; Alan M Zaslavsky; Eric J Feuer
Journal:  Med Decis Making       Date:  2010-05-18       Impact factor: 2.583

Review 3.  Screening for lung cancer.

Authors:  Renée Manser; Anne Lethaby; Louis B Irving; Christine Stone; Graham Byrnes; Michael J Abramson; Don Campbell
Journal:  Cochrane Database Syst Rev       Date:  2013-06-21

4.  The natural history of lung cancer estimated from the results of a randomized trial of screening.

Authors:  S D Walter; A Kubik; D M Parkin; J Reissigova; M Adamec; M Khlat
Journal:  Cancer Causes Control       Date:  1992-03       Impact factor: 2.506

5.  A smoking-based carcinogenesis model for lung cancer risk prediction.

Authors:  Millennia Foy; Margaret R Spitz; Marek Kimmel; Olga Y Gorlova
Journal:  Int J Cancer       Date:  2011-03-29       Impact factor: 7.396

6.  Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

Authors:  Xiaohong Li; Patricia L Blount; Thomas L Vaughan; Brian J Reid
Journal:  PLoS Comput Biol       Date:  2011-02-24       Impact factor: 4.475

7.  Use of tumor diameter to estimate the growth kinetics of cancer and sensitivity of screening tests.

Authors:  N Yamaguchi; T Yanagawa; T Yoshimura; N Kohrogi; K Tanaka; Y Nakamura; T Okubo
Journal:  Environ Health Perspect       Date:  1990-07       Impact factor: 9.031

8.  Modeling the natural history and detection of lung cancer based on smoking behavior.

Authors:  Xing Chen; Millennia Foy; Marek Kimmel; Olga Y Gorlova
Journal:  PLoS One       Date:  2014-04-04       Impact factor: 3.240

  8 in total

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