Dataset Information

Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population.

ABSTRACT:

Background

Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population.

Methods

During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with P_entry = .15 and P_stay = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability.

Results

A total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (P_HL = .689) and all subgroups.

Conclusions

We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.

SUBMITTER: Lyu Z

PROVIDER: S-EPMC7286442 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population.

Lyu Zhangyan Z Li Ni N Chen Shuohua S Wang Gang G Tan Fengwei F Feng Xiaoshuang X Li Xin X Wen Yan Y Yang Zhuoyu Z Wang Yalong Y Li Jiang J Chen Hongda H Lin Chunqing C Ren Jiansong J Shi Jufang J Wu Shouling S Dai Min M He Jie J

Cancer medicine 20200406 11

<h4>Background</h4>Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population.<h4>Methods</h4>During 2006 and 2015, a tot ...[more]

PMID: 32253829

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Dataset Information

Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population.

Background

Methods

Results

Conclusions

Publications

Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population.

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