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

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.