Imaging and clinical risk factors and predictive models for lymph node metastasis in patients with resectable lung adenocarcinoma
- VernacularTitle:可切除肺腺癌淋巴结转移的影像学和临床危险因素分析及预测模型构建
- Author:
Minhao LUO
1
;
Qinmin HAO
1
;
Fen WANG
2
;
Mei YUAN
1
Author Information
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
- Publication Type:Journal Article
- Keywords:
Lung adenocarcinoma;
lymph node metastasis;
risk factor;
spatial position;
predictive model
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(12):1764-1770
- CountryChina
- Language:Chinese
-
Abstract:
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 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 central/inner spatial location, solid density, pleural changes with wide base depression, and elevated serum CEA levels have a higher risk of lymph node metastasis.
