Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jul 1;25(1):1058.
doi: 10.1186/s12885-025-14449-6.

The probability of lung cancer in patients with pulmonary nodules detected via low-dose computed tomography screening in China

Lan-Wei Guo  1 , Zhang-Yan Lyu  2 , Yin Liu  1 , Qing-Cheng Meng  3 , Li-Yang Zheng  1 , Qiong Chen  1 , Hui-Fang Xu  1 , Rui-Hua Kang  1 , Shu-Zheng Liu  1 , Shao-Kai Zhang  4
Affiliations

The probability of lung cancer in patients with pulmonary nodules detected via low-dose computed tomography screening in China

Lan-Wei Guo et al. BMC Cancer. .

Abstract

Objective: Low dose computed tomography (LDCT) screening has been proven to be effective in reducing lung cancer mortality, but the ensuing high false-positive and overdiagnosis rates shackle the effectiveness of lung cancer screening (LCS) in China. Nodule malignancy prediction models may be an applicable solution.

Methods: We conducted a prospective cohort study to develop and internally validate the model using data from the ongoing Henan province Cancer Screening Program in Urban China (CanSPUC). From 2013 to 2021, 23,031 heavy smokers underwent baseline screening with LDCT; 2553 participants were diagnosed with pulmonary nodules. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Multivariable Cox proportional risk regression models were used to identify and integrate key prognostic factors for the development of a nomogram model. Data from the National Lung Screening Trial (NLST) were utilized for external validation.

Results: A total of 111 lung cancer cases with a median follow-up duration of 3.7 years occurred in the Henan CanSPUC. Age, gender, physical activity, consumption of pickled food, history of silicosis or pneumoconiosis, nodule type, size, calcification, and pleural retraction sign were included into the model. The AUC was 0.855, 0.844, and 0.863 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. Compared with Mayo model, VA model, PKU model, and Brock model, the Henan CanSPUC model yield statistically better discriminatory performance (all P values < 0.05). The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups. The model demonstrated good calibration and discrimination in the internal validation cohort, while the external validation cohort showed lower predictive performance, indicating that further external validation is needed.

Conclusions: The model developed and validated in this study may be used to estimate the probability of lung cancer in nodules detected at baseline LDCT, allowing more efficient risk-adapted follow-up in population-based LCS programs. However, further external validation in broader and more diverse populations is warranted.

Keywords: Lung Cancer; Prospective screening cohort; Pulmonary nodules; Risk assessment.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was conducted following the guidelines of the Helsinki Declaration and was approved by the Medical Ethics Committee of the Henan Cancer Hospital. Written informed consent forms were obtained from all participants. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Role of the funder: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Figures

Fig. 1
Fig. 1
Flow chart of participants included in this analysis
Fig. 2
Fig. 2
Nomogram to calculate the personal 1-, 3- and 5-year risk of lung cancer risk
Fig. 3
Fig. 3
The lung cancer incidence across different cancer risk categories
Fig. 4
Fig. 4
The receiver operating characteristic curves of prediction models in the training set
Fig. 5
Fig. 5
The receiver operating characteristic curves of prediction models in the validation set. (A) Internal validation; (B) External validation
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. - PubMed
    1. Zeng H, Zheng R, Sun K, Zhou M, Wang S, Li L, et al. Cancer survival statistics in China 2019–2021: a multicenter, population-based study. J Natl Cancer Cent. 2024;4(3):203–13. - PMC - PubMed
    1. Passiglia F, Cinquini M, Bertolaccini L, Del Re M, Facchinetti F, Ferrara R, et al. Benefits and harms of lung Cancer screening by chest computed tomography: A systematic review and Meta-Analysis. J Clin Oncol. 2021;39(23):2574–85. - PubMed
    1. Field JK, Vulkan D, Davies MP, Baldwin DR, Brain KE, Devaraj A, et al. Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis. Lancet Reg Heal. 2021;10:100179. - PMC - PubMed
    1. De Koning HJ, Van Der Aalst CM, De Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, et al. Reduced Lung-Cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020;382(6):503–13. - PubMed