Literature DB >> 18400331

Estimation of mean sojourn time for lung cancer by chest X-ray screening with a Bayesian approach.

Chun-Ru Chien1, Mei-Shu Lai, Tony Hsiu-Hsi Chen.   

Abstract

Very few studies, particularly from oriental population, reported the progression of lung cancer from asymptomatic to symptomatic phase. The present study aimed to estimate mean sojourn time (MST) of lung cancer, an average duration period in which tumour can be asymptotically detected by chest X-ray (CXR), taking into account gender, smoking and histological type. Based on institutional cancer registry for lung cancer patients with prior non-diagnostic CXR (n=221), data were collected on demographic features, histology type, survival status, history of smoking, and asymptomatic or symptomatic status in light of chief complaint at diagnosis retrieved from medical records. The MST for the natural history of lung cancer underpinning a three-state Markov model was estimated with a Bayesian approach. The estimated MST for lung cancer was 5.51 months (95% credible interval: 4.04-7.12). Small cell lung carcinoma was even statistically significantly shorter MST than non-small cell lung carcinoma (3.01 (3-3.98) months vs. 6.07 (4.44-8.25) months). In parallel with literatures reporting tumour growth rate related to CXR and computed tomography (CT), the shorter mean sojourn time by using CXR estimated in our study strongly suggests that CT screening may be more effective in early detection of lung cancer in population-based screening.

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Year:  2008        PMID: 18400331     DOI: 10.1016/j.lungcan.2008.02.020

Source DB:  PubMed          Journal:  Lung Cancer        ISSN: 0169-5002            Impact factor:   5.705


  6 in total

1.  Sojourn time and lead time projection in lung cancer screening.

Authors:  Dongfeng Wu; Diane Erwin; Gary L Rosner
Journal:  Lung Cancer       Date:  2010-11-13       Impact factor: 5.705

2.  Sensitivity of chest X-ray for detecting lung cancer in people presenting with symptoms: a systematic review.

Authors:  Stephen H Bradley; Sarah Abraham; Matthew Ej Callister; Adam Grice; William T Hamilton; Rocio Rodriguez Lopez; Bethany Shinkins; Richard D Neal
Journal:  Br J Gen Pract       Date:  2019-11-28       Impact factor: 5.386

3.  A Bayesian model for estimating multi-state disease progression.

Authors:  Shiwen Shen; Simon X Han; Panayiotis Petousis; Robert E Weiss; Frank Meng; Alex A T Bui; William Hsu
Journal:  Comput Biol Med       Date:  2016-12-22       Impact factor: 4.589

4.  Inference of Sojourn Time and Transition Density using the NLST X-ray Screening Data in Lung Cancer.

Authors:  Farhin Rahman; Dongfeng Wu
Journal:  Med Res Arch       Date:  2021-05-25

5.  Multistate models for the natural history of cancer progression.

Authors:  Li C Cheung; Paul S Albert; Shrutikona Das; Richard J Cook
Journal:  Br J Cancer       Date:  2022-07-11       Impact factor: 9.075

6.  Modeling progression in radiation-induced lung adenocarcinomas.

Authors:  Hatim Fakir; Werner Hofmann; Rainer K Sachs
Journal:  Radiat Environ Biophys       Date:  2010-01-08       Impact factor: 1.925

  6 in total

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