Differential diagnosis model construction of invasive degree of lung adenocarcinoma manifesting as ground-glass nodules with no or little solid component based on energy spectrum CT features
10.3760/cma.j.cn371439-20240215-00033
- VernacularTitle:基于能谱CT特征的无或少实性成分磨玻璃结节样肺腺癌浸润程度鉴别诊断模型构建
- Author:
Yiyong LIU
1
;
Fengzhi HUO
Author Information
1. 中国人民解放军联勤保障部队第九六九医院影像科，呼和浩特 010051
- Keywords:
Tomography, X-ray computed;
Adenocarcinoma of lung;
Neoplasm invasiveness;
Dagnosis, differential;
Ground-glass nodules
- From:
Journal of International Oncology
2025;52(4):197-201
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct the differential diagnosis model of invasive degree of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) with no or little solid component based on energy spectrum CT features, and to provide reference for the follow-up clinical diagnosis and treatment.Methods:A retrospective study was conducted on 145 patients who underwent surgical treatment for lung adenocarcinoma at the 969th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army and Inner Mongolia Hospital of Peking University Cancer Hospital from January 2019 to December 2022, presenting with CT findings of no or little solid component GGNs. The patients were divided into invasive group (51 cases) and microinvasive group (94 cases) based on the invasive degree. Logistic regression was used to conduct a multivariate analysis of factors affecting the differential diagnosis of invasive degree of lung adenocarcinoma manifesting as GGNs with no or little solid component, and construct a logistic regression model. Receiver operator characteristic (ROC) curve was used to analyze the predictive efficiency of each index.Results:Univariate analysis showed that the maximum diameter of nodules ( t=-6.30, P<0.001) , average CT value of nodules ( t=-5.43, P<0.001) , air bronchial sign ( χ2=23.21, P<0.001) , microvascular CT imaging type ( χ2=27.94, P<0.001) were predictors of invasive degree of lung adenocarcinoma manifesting as GGNs with no or little solid component. Multivariate logistic regression analysis showed that the maximum diameter of nodules ( OR=1.72, 95% CI: 1.33-2.23, P<0.001) , average CT value of nodules ( OR=1.01, 95% CI: 1.01-1.02, P<0.001) , air bronchial sign ( OR=4.92, 95% CI: 1.59-15.21, P=0.006) and microvascular CT imaging type Ⅲ ( OR=14.01, 95% CI: 2.97-66.06, P=0.001) were independent predictors of invasive degree of lung adenocarcinoma manifesting as GGNs with no or little solid component. A logistic regression model was constructed based on the results of the above multiple factor analysis: logit ( P) =0.54×maximum diameter of the nodule+0.01×average CT value of the nodule+1.59×air bronchogram sign+2.64×microvascular CT imaging type (type Ⅲ) -3.33. ROC curve analysis showed that the areas under the curve for differential diagnosis of invasive degree of lung adenocarcinoma manifesting as GGNs with no or little solid component based on the maximum diameter of nodules, average CT value of nodules, air bronchogram sign, microvascular CT imaging type, and logistic regression model P-value were 0.759, 0.751, 0.686, 0.741, and 0.918, respectively. Conclusions:The energy spectrum CT features, including the maximum diameter of nodules, average CT value of nodules, air bronchial sign, and microvascular CT imaging type, can be used for differential diagnosis of invasive degree of lung adenocarcinoma manifesting as GGNs with no or little solid component. The logistic regression model constructed using the above four factors has shown good performance in predicting the invasive degree of patient.
