A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China. (January 2022)

• Record Type: Journal Article
• Authors: Guo, Lan-Wei; Lyu, Zhang-Yan; Meng, Qing-Cheng; Zheng, Li-Yang; Chen, Qiong; Liu, Yin; Xu, Hui-Fang; Kang, Rui-Hua; Zhang, Lu-Yao; Cao, Xiao-Qin; Liu, Shu-Zheng; Sun, Xi-Bin; Zhang, Jian-Gong; Zhang, Shao-Kai
• Is Part Of: Lung cancer. Volume 163(2022)
• Page Start: 27
• Page End: 34
• Publication Date: 2022-01
• Subjects: LDCT low dose computed tomography -- USPSTF the United States Preventive Services Task Force -- CanSPUC the Cancer Screening Program in Urban China -- STROBE the Strengthening the Reporting of Observational Studies in Epidemiology -- ICD-O-3 the International Classification of Diseases for Oncology, 3rd edition -- ICD-10 the International Statistical Classification of Diseases and Related Health Problems, 10th edition -- BMI body mass index -- HR hazard ratio -- CI confidence interval -- ROC receiver-operating characteristic -- C-statistics concordance statistics -- AUC rea under curvea -- pyrs person-years -- COPD chronic obstructive pulmonary disease -- LLP Liverpool Lung Project -- PLCO Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial -- SNPs single nucleotide polymorphisms -- IQR interquartile range; Lung cancer -- Prospective cohort -- Risk assessment; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2021.11.015
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: In a large prospective lung cancer screening cohort study, we developed and internally validated a simple risk prediction model for lung cancer. Our results showed that the model has moderate discriminatory accuracy and goodness-of-fit for both men and women, smokers and never-smokers. The model has potential utility for shared decision-making and individualized risk assessment for tailored lung cancer screening. Abstract: Objective: Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer. Materials and methods: Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282, 254 participants including 126, 445 males and 155, 809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively. Results: By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100, 000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups. Conclusions: We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.