The value of CT features in predicting the invasion and invasive degree of lung pure ground-glass nodules based on the new classification of lung tumor in 2021
10.3760/cma.j.cn112149-20210707-00641
- VernacularTitle:CT特征预测基于2021年肺肿瘤新分类肺纯磨玻璃结节浸润性及浸润程度的价值
- Author:
Lin GAO
1
;
Jing ZHANG
;
Hui GU
;
Bing KANG
;
Xinxin YU
;
Shuai ZHANG
;
Yan GAO
;
Fanfan CAI
;
Ruopeng WANG
;
Ximing WANG
Author Information
1. 山东第一医科大学附属省立医院影像科，济南 250021
- Keywords:
Lung neoplasms;
Tomography, X-ray computed;
Pathology
- From:
Chinese Journal of Radiology
2022;56(6):616-622
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of CT features in predicting the invasion and degree of invasiveness of lung pure ground-glass nodules (pGGN) in the new histological classification in 2021.Methods:A total of 281 patients (304 lesions) with pGGN confirmed by surgical pathology from December 2018 to January 2021 in Shandong Provincial Hospital Affiliated to Shandong First Medical University were retrospectively analyzed. According to the pathological types, the patients were divided into prodromal lesion group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), 129 cases], minimally invasive group [minimally invasive adenocarcinoma (MIA), 116 cases] and invasive group [invasive adenocarcinoma (IAC), 59 cases]. Clinical data (age, gender, smoking history, family history of cancer), and CT parameters [shape, boundary, lobulation, burr, vacuolar sign, bronchial abnormality sign, internal vessel sign, pleural traction sign, longest diameter, shortest diameter, unenhanced CT value, contrast-enhanced CT value in arterial phase, contrast-enhanced CT values in venous phase, the degree of enhancement (ΔCT A-N, ΔCT V-N)] were recorded and measured. The ANOVA, Kruskal-Wallis H and χ 2 test were used to compare the differences among the three groups. Binary logistic regression analysis was used to evaluate the independent risk factors of nodular invasion [prodromal lesion and invasive lesion (MIA and IAC)] and the degree of nodular invasion (MIA and IAC), and receiver operating characteristic (ROC) curve analysis was performed for each parameter. Results:There were statistically significant differences in age, pGGN morphology, lobulation, vacuolar sign, bronchial abnormality sign, internal vascular sign, pleural traction sign, longest diameter, shortest diameter, unenhanced CT value, contrast-enhanced CT value in arterial phase, contrast-enhanced CT value in venous phase among the precursor lesion group, minimally invasive group and invasive group ( P<0.05). Binary logistic regression analysis showed that vacuole sign (OR=2.832, 95%CI 1.363-5.887, P=0.005), internal vascular sign (OR=3.021, 95%CI 1.909-4.779, P<0.001) and unenhanced CT value (OR=1.003, 95%CI 1.001-1.006, P=0.019) were independent risk factors for invasion. Lobulation (OR=5.739, 95%CI 2.735-12.042, P<0.001), internal vascular sign (OR=1.968, 95%CI 1.128-3.433, P=0.017) and unenhanced CT value (OR=1.004, 95%CI 1.001-1.008, P=0.012) were independent risk factors for the degree of invasiveness. ROC curve analysis showed that the efficiency of internal vascular sign was the highest in distinguishing precursor lesion and the invasive, the area under the curve (AUC) was 0.757, the sensitivity was 50.3%, the specificity was 89.8%. The efficiency of lobulation was the highest in distinguishing MIA and IAC (AUC=0.702), with a sensitivity of 61.0% and specificity of 79.3%. Conclusions:CT features are of certain value in predicting the invasion and degree of invasiveness of lung pGGN in the new histological classification in 2021, and internal vascular sign is more effective in predicting the invasion of lung pGGN. Lobulation can predict the degree of invasiveness of pGGN better.
