scholarly journals A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in china

Lung Cancer ◽  
2021 ◽  
Author(s):  
Lan-Wei Guo ◽  
Zhang-Yan Lyu ◽  
Qing-Cheng Meng ◽  
Li-Yang Zheng ◽  
Qiong Chen ◽  
...  
Lung Cancer ◽  
2020 ◽  
Vol 148 ◽  
pp. 79-85
Author(s):  
Mark R. Waddle ◽  
Stephen J. Ko ◽  
Jackson May ◽  
Tasneem Kaleem ◽  
Daniel H. Miller ◽  
...  

2019 ◽  
Vol 80 (5) ◽  
pp. 860
Author(s):  
Tae Jung Kim ◽  
Hyae Young Kim ◽  
Jin Mo Goo ◽  
Joo Sung Sun

Lung Cancer ◽  
2021 ◽  
Vol 156 ◽  
pp. 31-40
Author(s):  
Martin C. Tammemägi ◽  
Gail E. Darling ◽  
Heidi Schmidt ◽  
Diego Llovet ◽  
Daniel N. Buchanan ◽  
...  

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3496
Author(s):  
Yohwan Yeo ◽  
Dong Wook Shin ◽  
Kyungdo Han ◽  
Sang Hyun Park ◽  
Keun-Hye Jeon ◽  
...  

Early detection of lung cancer by screening has contributed to reduce lung cancer mortality. Identifying high risk subjects for lung cancer is necessary to maximize the benefits and minimize the harms followed by lung cancer screening. In the present study, individual lung cancer risk in Korea was presented using a risk prediction model. Participants who completed health examinations in 2009 based on the Korean National Health Insurance (KNHI) database (DB) were eligible for the present study. Risk scores were assigned based on the adjusted hazard ratio (HR), and the standardized points for each risk factor were calculated to be proportional to the b coefficients. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability assessed by plotting the mean predicted probability against the mean observed probability of lung cancer. Among candidate predictors, age, sex, smoking intensity, body mass index (BMI), presence of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis (TB), and type 2 diabetes mellitus (DM) were finally included. Our risk prediction model showed good discrimination (c-statistic, 0.810; 95% CI: 0.801–0.819). The relationship between model-predicted and actual lung cancer development correlated well in the calibration plot. When using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding lung cancer screening or lifestyle modification, including smoking cessation.


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