Value of nomogram based on dual-layer detector spectral CT quantitative parameters and conventional CT features for evaluating high-grade pattern of pulmonary invasive non-mucinous adenocarcinoma
10.3760/cma.j.cn112149-20230907-00166
- VernacularTitle:双层探测器光谱CT定量参数联合常规CT特征列线图评估含高级别亚型成分肺浸润性非黏液腺癌的价值
- Author:
Rong HONG
1
;
Xiaoxia PING
;
Su HU
;
Xing XIONG
;
Yang YU
;
Chunhong HU
Author Information
1. 苏州大学附属第一医院放射科，苏州 215006
- Keywords:
Lung neoplasms;
Invasive non-mucinous adenocarcinoma;
Tomography, X-ray computed;
Nomogram
- From:
Chinese Journal of Radiology
2024;58(2):187-193
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of nomogram based on dual-layer detector spectral CT quantitative parameters and conventional CT feature in evaluating high-grade pattern (HGP) of pulmonary invasive non-mucinous adenocarcinoma.Methods:This study was a case-control study. A total of 71 patients with pathologically confirmed pulmonary invasive non-mucinous adenocarcinoma in the First Affiliated Hospital of Soochow University from February 2022 to May 2023 were retrospectively enrolled, which were divided into HGP and non-HGP groups according to pathological results. Conventional CT features were analyzed, including size, shape, density, internal signs, margins, and pleural retraction. The iodine concentration (IC), electron density (ED), and normalized iodine concentration (NIC) of the lesions in both the arterial phase (AP) and venous phase (VP) were measured. Differences between the two groups were analyzed using independent sample t-test, Mann-Whitney U test, or χ2 test. Multivariate logistic regression analysis was used to select the independent influencing factors of HGP in pulmonary invasive non-mucinous adenocarcinoma, and the conventional CT feature model, the spectral CT quantitative parameter model, and the combined model were constructed and expressed in a nomogram. The area under the curve (AUC) of receiver operating characteristic curve was used to assess the performance of each model, and was compared by DeLong test. Decision curves (DCA) was used to assess the clinical net benefit of the models. Results:There were significant differences between HGP group and non-HGP group in terms of density, lobulation, spiculation, IC AP, IC VP, NIC AP, ED AP and ED VP (all P<0.05). The multivariate logistic regression analysis showed that the solid nodule ( OR=15.452, 95% CI 4.246-56.235, P<0.001), lobulation ( OR=7.069, 95% CI 1.618-30.883, P=0.009), ED AP( OR=1.183, 95% CI 1.064-1.315, P=0.002) and IC VP ( OR=0.231, 95% CI 0.072-0.744, P=0.014) were independent influencing factors for predicting HGP in pulmonary invasive non-mucinous adenocarcinoma. The AUC of the conventional CT feature model, spectral CT quantitative parameter model, and the combined model were 0.835, 0.890, and 0.915, respectively. The AUC of the combined model was better than that of the conventional CT feature model ( Z=2.67, P=0.008). The DCA analysis demonstrated that the nomogram had higher clinical net benefit than the conventional CT feature model. Conclusions:The nomogram based on the quantitative parameters of dual-layer detector spectral CT and conventional CT features have favorable diagnostic efficacy in predicting HGP in pulmonary invasive non-mucinous adenocarcinoma, and can be used as a reliable tool for non-invasive diagnosis of HGP before surgery.
