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An individual risk prediction model for lung cancer based on a study in a Chinese population

Abstract

Aims and Background

Early detection and diagnosis remains an effective yet challenging approach to improve the clinical outcome of patients with cancer. Low-dose computed tomography screening has been suggested to improve the diagnosis of lung cancer in high-risk individuals. To make screening more efficient, it is necessary to identify individuals who are at high risk.

Methods and Study design

We conducted a case-control study to develop a predictive model for identification of such high-risk individuals. Clinical data from 705 lung cancer patients and 988 population-based controls were used for the development and evaluation of the model. Associations between environmental variants and lung cancer risk were analyzed with a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic curve and the optimal operating point.

Results

Our results indicate that lung cancer risk factors included older age, male gender, lower education level, family history of cancer, history of chronic obstructive pulmonary disease, lower body mass index, smoking cigarettes, a diet with less seafood, vegetables, fruits, dairy products, soybean products and nuts, a diet rich in meat, and exposure to pesticides and cooking emissions. The area under the curve was 0.8851 and the optimal operating point was obtained. With a cutoff of 0.35, the false positive rate, true positive rate, and Youden index were 0.21, 0.87, and 0.66, respectively.

Conclusions

The risk prediction model for lung cancer developed in this study could discriminate high-risk from low-risk individuals.

Tumori 2015; 101(1): 16 - 23

Article Type: ORIGINAL RESEARCH ARTICLE

DOI:10.5301/tj.5000205

Authors

Xu Wang, Kewei Ma, Jiuwei Cui, Xiao Chen, Lina Jin, Wei Li

Article History

Disclosures

Financial support: The principal funding for this study was provided by the Key Clinical Project of the Ministry of Health of the People’s Republic of China (2010-2012) (Grant No. 2001133). The study was also funded by the Jilin Provincial Science and Technology Department (2012-2014) (Grant No 201201023). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Conflict of interest: The authors declare that there are no conflicts of interest.

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Authors

Affiliations

  • Cancer Center, First Hospital of Jilin University, Changchun, Jilin Province - China
  • School of Public Health, Jilin University, Changchun, Jilin Province - China

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