Prediction of Lymph Node Metastasis of Mixed Ground-glass Nodules
Based on Clinical Imaging Information.
10.3779/j.issn.1009-3419.2023.101.06
- Author:
Jian GAO
1
;
Qingyi QI
2
;
Hao LI
1
;
Jie YU
3
;
Jian ZHANG
1
;
Bingbing LIN
2
;
Xiao LI
1
;
Nan HONG
2
;
Yun LI
1
Author Information
1. Department of Thoracic Surgery, Thoracic Oncology Institute, Peking University People's Hospital, Beijing 100044, China.
2. Department of Radiology, Peking University People's Hospital, Beijing 100044, China.
3. Qingdao Women and Children's Hospital, Qingdao 266034, China.
- Publication Type:Journal Article
- Keywords:
Lymph node metastasis;
Mixed ground-glass nodules;
Ratio of solid component
- MeSH:
Humans;
Lymphatic Metastasis;
Lung Neoplasms;
Adenocarcinoma;
Lymph Nodes;
Social Group
- From:
Chinese Journal of Lung Cancer
2023;26(2):113-118
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND:Previous studies have shown that lymph node metastasis only occurs in some mixed ground-glass nodules (mGGNs) which the pathological results were invasive adenocarcinoma (IAC). However, the presence of lymph node metastasis leads to the upgrading of tumor-node-metastasis (TNM) stage and worse prognosis of the patients, so it is important to perform the necessary evaluation before surgery to guide the operation method of lymph node. The aim of this study was to find suitable clinical and radiological indicators to distinguish whether mGGNs with pathology as IAC is accompanied by lymph node metastasis, and to construct a prediction model for lymph node metastasis.
METHODS:From January 2014 to October 2019, the patients with resected IAC appearing as mGGNs in computed tomography (CT) scan were reviewed. All the lesions were divided into two groups (with lymph node metastasis or not) according to their lymph node status. Lasso regression model analysis by applying R software was used to evaluate the relationship between clinical and radiological parameters and lymph node metastasis of mGGNs.
RESULTS:A total of 883 mGGNs patients were enroled in this study, among which, 12 (1.36%) showed lymph node metastasis. Lasso regression model analysis of clinical imaging information in mGGNs with lymph node metastasis showed that previous history of malignancy, mean density, mean density of solid components, burr sign and percentage of solid components were informative. Prediction model for lymph node metastasis in mGGNs was developed based on the results of Lasso regression model with area under curve=0.899.
CONCLUSIONS:Clinical information combined with CT imaging information can predict lymph node metastasis in mGGNs.
