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

Lung Cancer : Journal of the International Association for the Study of Lung Cancer
Chun-Ru ChienTony 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...Continue Reading

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Citations

Jan 9, 2010·Radiation and Environmental Biophysics·Hatim FakirRainer K Sachs
Nov 16, 2010·Lung Cancer : Journal of the International Association for the Study of Lung Cancer·Dongfeng WuGary L Rosner
Mar 17, 2010·Statistical Methods in Medical Research·Z UhryS W Duffy
Oct 23, 2019·The British Journal of General Practice : the Journal of the Royal College of General Practitioners·Stephen H BradleyRichard D Neal
Feb 27, 2021·International Journal of Cancer. Journal International Du Cancer·Agne Krilaviciute, Hermann Brenner
Dec 31, 2016·Computers in Biology and Medicine·Shiwen ShenWilliam Hsu

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