scholarly journals Diagnostic value and imaging features of multi-detector CT in lung adenocarcinoma with ground glass nodule patients

2020 ◽  
Vol 20 (1) ◽  
pp. 693-698
Author(s):  
Jun Lu ◽  
Haitao Tang ◽  
Xinguo Yang ◽  
Lei Liu ◽  
Minxia Pang
2019 ◽  
Vol 30 (4) ◽  
pp. 1847-1855 ◽  
Author(s):  
Jing Gong ◽  
Jiyu Liu ◽  
Wen Hao ◽  
Shengdong Nie ◽  
Bin Zheng ◽  
...  

2021 ◽  
Author(s):  
Ziyi Wang ◽  
Lindan Zuo ◽  
Zhimin Liao ◽  
Wei Zheng ◽  
Qi Hu ◽  
...  

Abstract Background Pure ground-glass nodules are considered to be radiologically noninvasive in lung adenocarcinoma. However, some pure ground-glass nodules are found to be invasive adenocarcinoma pathologically. This study aimed to find out the correlation between the clinical imaging features and the degree of invasion of pulmonary pure ground glass nodules (≤ 3cm). Methods The clinical data of 886 patients who underwent minimally invasive surgery for pulmonary nodules from June 2013 to June 2016 were collected. Among them, 72 patients had complete clinical data and isolated pulmonary ground glass nodule resection, and the diameter of pulmonary ground glass nodule was less than or equal to 3 cm. Results A total of 72 eligible patients were included in the study. Univariate analysis showed that there were significant differences in carcinoembryonic antigen, maximum diameter and area of pure ground glass nodules in patients with pre-invasive lesions and invasive lesions(P < 0.05). Multivariate logistic regression analysis showed that there were only statistical differences in the maximum diameter of nodule pre-invasive lesions and invasive lesions. The optimal cutoff value for CT-maximal diameter to predict pre-invasive lesions or invasive lesions was 1.08cm. Conclusion It is reliable to predict the pathological types of nodules (pre-invasive and invasive) by measuring the maximum diameter of pure ground glass nodules, and the most reliable cut-off value is 1.08cm.


2020 ◽  
Vol 9 (3) ◽  
pp. 1660-1669
Author(s):  
Nan Zhang ◽  
Jun-Feng Liu ◽  
Ya-Ning Wang ◽  
Li Yang

Sign in / Sign up

Export Citation Format

Share Document