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

Zhangyan Lyu; Ni Li; Shuohua Chen; Gang Wang; Fengwei Tan; Xiaoshuang Feng; Xin Li; Yan Wen; Zhuoyu Yang; Yalong Wang; Jiang Li; Hongda Chen; Chunqing Lin; Jiansong Ren; Jufang Shi; Shouling Wu; Min Dai; Jie He

doi:10.1002/cam4.3025

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

Cancer Med. 2020 Jun;9(11):3983-3994. doi: 10.1002/cam4.3025. Epub 2020 Apr 6.

Authors

Zhangyan Lyu¹, Ni Li¹, Shuohua Chen², Gang Wang³, Fengwei Tan⁴, Xiaoshuang Feng¹, Xin Li¹, Yan Wen¹, Zhuoyu Yang¹, Yalong Wang⁴, Jiang Li¹, Hongda Chen¹, Chunqing Lin¹, Jiansong Ren¹, Jufang Shi¹, Shouling Wu², Min Dai¹, Jie He⁴

Affiliations

¹ Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
² Department of Oncology, Kailuan General Hospital, Tangshan, China.
³ Health Department of Kailuan (Group), Tangshan, China.
⁴ Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

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.

Keywords: lung cancer; metabolic markers; prospective study; risk prediction model.

Publication types

Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Aged
Asian People / statistics & numerical data*
Biomarkers, Tumor / metabolism*
China / epidemiology
Female
Follow-Up Studies
Humans
Incidence
Lung Neoplasms / epidemiology*
Lung Neoplasms / metabolism
Male
Metabolome*
Middle Aged
Models, Statistical*
Prognosis
Prospective Studies
Risk Assessment
Risk Factors

Substances

Biomarkers, Tumor