Computed tomography features and prediction model of stage-IA solitary nodular invasive mucinous lung adenocarcinoma
10.13491/j.issn.1004-714X.2023.02.016
- VernacularTitle:IA期孤立结节型浸润性粘液性肺腺癌的CT特征及预测模型的建立
- Author:
Lei ZHANG
1
;
Wenrong SHEN
1
;
Xiuming ZHANG
1
;
Shaorong YU
1
;
Jiuyan JIANG
1
;
Mengjie WU
1
;
Dan SHI
1
;
Na YIN
1
Author Information
1. Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009 China.
- Publication Type:Journal Article
- Keywords:
Invasive mucinous lung adenocarcinoma;
Influencing factor;
Risk score model
- From:
Chinese Journal of Radiological Health
2023;32(2):171-175
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the computed tomography (CT) features of solitary nodular invasive mucinous lung adenocarcinoma (IMA) in stage IA and establish its prediction model. Methods We included 53 lesions of 53 patients with stage-IA IMA and 141 control lesions of 141 patients with invasive non-mucinous lung adenocarcinoma (NIMA) that were confirmed by surgical pathology in our hospital from January 2017 to December 2019. Univariable analysis was used to compare the demographics and CT signs of the two groups. Multivariable logistic regression analysis was performed to determine the main factors influencing solitary nodular IMA. A risk score prediction model was constructed based on the regression coefficients of the main influencing factors. A receiver operating characteristic (ROC) curve was used to assess the performance of the model. Results The univariable analysis showed significant differences between the two groups in age, largest nodule diameter, tumor-lung interface, lobulation, spiculation, air-bronchogram or vacuole sign, vessel abnormalities (P < 0.05). The spiculation sign was different between the two groups, which was longer and softer in the IMA group while shorter and harder in the NIMA group. There was no significant difference in sex, nodule shape, or pleural retraction (P > 0.05), but irregular shapes were slightly more frequent in the IMA group. The multivariable logistic regression analysis showed that obscure tumor-lung interface (odds ratio (OR = 20.930, P < 0.05), air-bronchogram or vacuole sign (OR = 7.126, P < 0.05), spiculation sign (OR = 4.207, P < 0.05), and vessel abnormalities (OR = 0.147, P < 0.05) were the main influencing factors. The prediction model based on those factors’ regression coefficients had an area under the ROC curve of 0.829 (P < 0.05). Conclusion Compared with those with NIMA, patients with solitary nodular IMA in stage IA were older and more likely to have the CT features of obscure tumor-lung interface, air-bronchogram or vacuole sign, and longer and softer spiculation. Based on the regression coefficients of tumor-lung interface, air-bronchogram or vacuole sign, spiculation, and vessel abnormalities, the risk score prediction model showed good predictive performance for solitary nodular IMA.
