Imaging and clinical risk factors and predictive models for lymph node metastasis in patients with resectable lung adenocarcinoma_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LUO Minhao ¹ , HAO Qinmin ¹ , WANG Fen ² ,  YUAN Mei ¹

1. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, P. R. China;
2. Department of Medical Imaging, Huai'an First People's Hospital Affiliated to Nanjing Medical University, Huai'an, 223300, Jiangsu, P. R. China;

Corresponding author：

YUAN Mei, Email: yuanmeijiangsu@163.com

Keywords：

Lung adenocarcinoma; lymph node metastasis; risk factor; spatial position

DOI：

10.7507/1007-4848.202410005

Video：

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Objective To investigate the risk factors for lymph node metastasis in resectable lung adenocarcinoma by combining spatial location, clinical, and imaging features, and to construct a lymph node metastasis prediction model. Methods A retrospective study on patients who underwent chest computed tomography (CT) at the First Affiliated Hospital of Nanjing Medical University from June 2016 to June 2020 and were surgically confirmed to have invasive lung adenocarcinoma with or without lymph node metastasis was conducted. Patients were divided into a positive group and a negative group based on the presence or absence of lymph node metastasis. Clinical and imaging data of the patients were collected, and the independent risk factors for lymph node metastasis in resectable lung adenocarcinoma were analyzed using univariate and multivariate logistic regression. A combined spatial location-clinical-imaging feature prediction model for lymph node metastasis was established and compared with the traditional lymph node metastasis prediction model that does not include spatial location features. Results A total of 611 patients were included, with 333 in the positive group, including 172 males and 161 females, with an average age of (58.9±9.7) years; and 278 in the negative group, including 127 males and 151 females, with an average age of (60.1±11.4) years. Univariate and multivariate logistic regression analyses showed that the spatial relationship of the lesion to the lung hilum, nodule type, pleural changes, and serum carcinoembryonic antigen (CEA) levels were independent risk factors for lymph node metastasis. Based on this, the combined spatial location-clinical-imaging feature prediction model had a sensitivity of 91.67%, specificity of 74.05%, accuracy of 87.88%, and area under the curve (AUC) of 0.885. The traditional lymph node metastasis prediction model, which did not include spatial location features, had a sensitivity of 76.40%, specificity of 72.10%, accuracy of 53.86%, and AUC of 0.827. The difference in AUC between the two prediction methods was statistically significant (P=0.026). Compared with the traditional prediction model, the predictive performance of the combined spatial location-clinical-imaging feature prediction model was significantly improved. Conclusion In patients with resectable lung adenocarcinoma, those with basal spatial location, solid density, pleural changes with wide base depression, and elevated serum CEA levels have a higher risk of lymph node metastasis.

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22.	Zhang G, Zhao Z, Cao Y, et al. Relationship between epidermal growth factor receptor mutations and CT features in patients with lung adenocarcinoma. Clin Radiol, 2021, 76(6): 473. e17-473. e24.

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin, 2019, 69(1): 7-34.
2. 陈文达, 徐秋贞, 王涛, 等. 基于非小细胞肺癌原发灶影像组学的隐匿性淋巴结转移预测. 临床放射学杂志, 2022, 41(4): 643-649.Chen WD, Xu QZ, Wang T, et al. Prediction of occult lymph node metastasis based on radiomics of primary non-small cell lung cancer. J Clin Radiol, 2022, 41(4): 643-649.
3. 陈文达. 基于影像组学: 预测非小细胞肺癌隐匿性淋巴结转移. 东南大学, 2021.Chen WD. Radiomics-based prediction of occult lymph node metastasis in non-small cell lung cancer. Southeast University, 2021.
4. Liu M, Mu J, Song F, et al. Growth characteristics of early-stage (ⅠA) lung adenocarcinoma and its value in predicting lymph node metastasis. Cancer Imaging, 2023, 23(1): 115.
5. Yin G, Song Y, Li X, et al. Prediction of mediastinal lymph node metastasis based on 18F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer. Eur Radiol, 2021, 31(6): 3983-3992.
6. Belanger AR, Hollyfield J, Yacovone G, et al. Incidence and clinical relevance of non-small cell lung cancer lymph node micro-metastasis detected by staging endobronchial ultrasound-guided transbronchial needle aspiration. J Thorac Dis, 2019, 11(8): 3650-3658.
7. Silvestri GA, Gonzalez AV, Jantz MA, et al. Methods for staging non-small cell lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest, 2013, 143(5 Suppl): e211S-e250S.
8. National Comprehensive Cancer Network. Clinical practice guidelines in oncology–non-small cell lung cancer, version 6.2021. 2021. Available at: https://www.nccn.org/professionals/physician_gls/pdf/nscl.pdf. Accessed on October 2, 2024.
9. Cardillo G, Petersen RH, Ricciardi S, et al. European guidelines for the surgical management of pure ground-glass opacities and part-solid nodules: Task Force of the European Association of Cardio-Thoracic Surgery and the European Society of Thoracic Surgeons. Eur J Cardiothorac Surg, 2023, 64(4): ezad222.
10. Shin SH, Jeong DY, Lee KS, et al. Which definition of a central tumour is more predictive of occult mediastinal metastasis in nonsmall cell lung cancer patients with radiological N0 disease? Eur Respir J, 2019, 53(3): 1801508.
11. Casal RF, Sepesi B, Sagar AS, et al. Centrally located lung cancer and risk of occult nodal disease: An objective evaluation of multiple definitions of tumour centrality with dedicated imaging software. Eur Respir J, 2019, 53(5): 1802220.
12. Kawamoto N, Tsutani Y, Kamigaichi A, et al. Tumour location predicts occult N1 nodal metastasis in clinical stageⅠnon-small-cell lung cancer. Eur J Cardiothorac Surg, 2023, 63(2): ezac575.
13. 李响, 朱燚宁, 吴卫兵, 等. 肺结节三维空间位置判定方法及临床意义. 中国胸心血管外科临床杂志, 2021, 28(3): 305-310.Li X, Zhu YN, Wu WB, et al. The determination method and clinical significance of three-dimensional spatial position of pulmonary nodules. Chin J Thorac Cardiovasc Surg, 2021, 28(3): 305-310.
14. Huang J, Bian C, Zhang W, et al. Partitioning the lung field based on the depth ratio in three-dimensional space. Transl Lung Cancer Res, 2022, 11(6): 1165.
15. Wang T, Yang Y, Liu X, et al. Primary invasive mucinous adenocarcinoma of the lung: Prognostic value of CT imaging features combined with clinical factors. Korean J Radiol, 2021, 22(4): 652-662.
16. Kim H J, Cho J Y, Lee Y J, et al. Clinical significance of pleural attachment and indentation of subsolid nodule lung cancer. Cancer Res Treat. 2019, 51(4): 1540-1548.
17. Jiang Y, Xiong Z, Zhao W, et al. Pathological components and CT imaging analysis of the area adjacent pleura within the pure ground-glass nodules with pleural deformation in invasive lung adenocarcinoma. BMC cancer, 2022, 22(1): 958.
18. Wang Y, Jing L, Wang G. Risk factors for lymph node metastasis and surgical methods in patients with early-stage peripheral lung adenocarcinoma presenting as ground glass opacity. J Cardiothorac Surg, 2020, 15(1): 121.
19. Zheng Y, Ju S, Huang R, et al. Lymph node metastasis risk factors in clinical stage ⅠA3 lung adenocarcinoma. J Cancer Res Ther, 2023, 19(1): 34-38.
20. Sun G, Sun Y, Zou Z, et al. Analysis of segmental lymph node metastasis and clinical features in cT1N0M0 lung adenocarcinoma. Biomed Res Int, 2020, 2020: 2842604.
21. Sung P, Yoon SH, Kim J, et al. Bronchovascular bundle thickening on CT as a predictor of survival and brain metastasis in patients with stage ⅠA peripheral small cell lung cancer. Clin Radiol, 2021, 76(1): 76. e37-76. e46.
22. Zhang G, Zhao Z, Cao Y, et al. Relationship between epidermal growth factor receptor mutations and CT features in patients with lung adenocarcinoma. Clin Radiol, 2021, 76(6): 473. e17-473. e24.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

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