Clinical application and research progress of artificial intelligence-assisted diagnosis of pulmonary nodules_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIU Chen ¹ , FANG Zemin ^1,2 , SHAO Zuoliang ¹ , YU Ruoting ¹ ,  GAO Wei ¹

1. Department of Cardiothoracic Surgery, Tianyou Hospital Affiliated to Wuhan University of Science & Technology, Wuhan, 430064, P. R. China;
2. XX;

Corresponding author：

GAO Wei, Email: gw_0504@163.com

Keywords：

Artificial intelligence; computer-assisted diagnosis; pulmonary nodules; clinical application

DOI：

10.7507/1007-4848.202411026

Video：

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[Abstract ]Artificial intelligence (AI) has been widely used in all walks of life, including healthcare, and has shown great application value in the auxiliary diagnosis of pulmonary nodules in the medical field. In the face of a large amount of lung imaging data, clinicians use AI tools to identify lesions more quickly and accurately, improving work efficiency, but there are still many problems in this field, such as the high false positive rate of recognition, and the difficulty in identifying special types of nodules. Researchers and clinicians are actively developing and using AI tools to promote their continuous evolution and make them better serve human health. This article reviews the clinical application and research progress of AI-assisted diagnosis of pulmonary nodules.

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6. de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging, 2023, 104(1): 11-17.
7. Wang X, Peng Y, Lu L, et al. Chest X-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. IEEE, 2017: 1-9.
8. Irvin JA, Rajpurkar P, Ko M, et al. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison: AAAI Conference on Artificial Intelligence, 2019.
9. Johnson AEW, Pollard TJ, Berkowitz SJ, et al. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data, 2019, 6(1): 317.
10. Pehrson LM, Nielsen MB, Ammitzbøl Lauridsen C. Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI Database: A systematic review. Diagnostics (Basel), 2019, 9(1): 29.
11. Naseer I, Akram S, Masood T, et al. Performance analysis of state-of-the-art CNN Architectures for LUNA16. Sensors (Basel), 2022, 22(12): 4426.
12. Yan K, Wang X, Lu L, et al. DeepLesion: Automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med Imaging (Bellingham), 2018, 5(3): 036501.
13. Bustos A, Pertusa A, Salinas JM, et al. PadChest: A large chest X-ray image dataset with multi-label annotated reports. Med Image Anal, 2020 Dec: 66: 101797.
14. Nguyen HQ, Lam K, Le LT, et al. VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations. Sci Data, 2022, 9(1): 429.
15. National lung screening trial research team. Lung cancer incidence and mortality with extended follow-up in the national lung screening trial. J Thorac Oncol, 2019, 14(10): 1732-1742.
16. Grove O, Berglund AE, Schabath MB, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One, 2015, 10(3): e0118261.
17. Yang J, Veeraraghavan H, Armato SG, et al. Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med Phys, 2018, 45(10): 4568-4581.
18. Yan K, Wang X, Lu L, et al. DeepLesion: Automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med Imaging (Bellingham), 2018, 5(3): 036501.
19. 王子奇. 基于多维度数据新型肺结节良恶性鉴别诊断模型的建立和靶标研究. 郑州大学, 2022.WANG ZQ. Study on the novel predictive model and biomarkers for the differential diagnosis of pulmonary nodules based on multi-level data. Zhengzhou Univ, 2022.
20. Wu Z, Huang T, Zhang S, et al. A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: Evidence from a large Chinese southwestern population. J Cancer Res Clin Oncol, 2021, 147(1): 275-285.
21. Chen K, Nie Y, Park S, et al. Development and validation of machine learning-based model for the prediction of malignancy in multiple pulmonary nodules: Analysis from multicentric cohorts. Clin Cancer Res, 2021, 27(8): 2255-2265.
22. Yang D, Zhang X, Powell CA, et al. Probability of cancer in high-risk patients predicted by the protein-based lung cancer biomarker panel in China: LCBP study. Cancer, 2018, 124(2): 262-270.
23. Liu HY, Zhao XR, Chi M, et al. Risk assessment of malignancy in solitary pulmonary nodules in lung computed tomography: A multivariable predictive model study. Chin Med J (Engl), 2021, 134(14): 1687-1694.
24. 蒋捷, 刘锋, 王波, 等. CT联合肿瘤标志物预测肺结节低分化腺癌的模型构建. 中国胸心血管外科临床杂志, 2025, 32(1): 73-79.Jiang J, Liu F, Wang B, et al. Construction of a predictive model for poorly differentiated adenocarcinoma in pulmonary nodules using CT combined with tumor markers. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(1): s73-s79.
25. Chung K, Mets OM, Gerke PK, et al. Brock malignancy risk calculator for pulmonary nodules: validation outside a lung cancer screening population. Thorax, 2018, 73(9): 857-863.
26. 丁辉, 胡传贤, 黄苏, 等. 基于CT和18F-FDGPET/CT的肺癌风险预测模型对肺结节恶性风险的验证研究. 国际放射医学核医学杂志, 2019, 43(1): 17-21.Ding H, Hu CX, Huang S, et al. Verification of malignant risk of pulmonary nodules based on CT and 18F-FDG PET/CT prediction model. Intern J Rad Med Nuclear Med, 2019, 43(1): 17-21.
27. van Leeuwen KG, Schalekamp S, Rutten MJCM, et al. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol, 2021, 31(6): 3797-3804.
28. Milam ME, Koo CW. The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States. Clin Radiol, 2023, 78(2): 115-122.
29. Homayounieh F, Digumarthy S, Ebrahimian S, et al. An Artificial intelligence-based chest X-ray model on human nodule detection accuracy from a multicenter study. JAMA Netw Open, 2021, 4(12): e2141096.
30. van Leeuwen KG, Schalekamp S, Rutten MJCM, et al. Comparison of commercial AI software performance for radiograph lung nodule detection and bone age prediction. Radiology, 2024, 310(1): e230981.
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