Ye Jing, Ling Jun, Lv Yan, Chen Juan, Cai Junhui, Chen Mingxiang
Department of Medical Imaging, Yangzhou University Clinical College Subei People's Hospital, Yangzhou, Jiangsu 225002, P.R. China.
Exp Ther Med. 2020 Apr;19(4):2483-2490. doi: 10.3892/etm.2020.8511. Epub 2020 Feb 10.
The present study aimed to investigate the ability of CT-based texture analysis to differentiate invasive adenocarcinoma (IA) from pre-invasive lesions (PIL) or minimally IA (MIA) appearing as ground-glass opacity (GGO) nodules, and to further compare the performance of non-enhanced CT (NECT) images with that of contrast-enhanced CT (CECT) images. A total of 77 patients with GGO nodules and surgically confirmed pulmonary adenocarcinoma were included in the present retrospective study. Each GGO nodule was manually segmented and its texture features were extracted from NECT and CECT images using in-house developed software coded in MATLAB (MathWorks). The independent-samples ttest was used to select the texture features with statistically significant differences between IA and MIA/PIL. Multivariate logistic regression and receiver operating characteristics (ROC) curve analyses were performed to identify predictive features. Of the 77 GGO nodules, 12 were atypical adenomatous hyperplasia or adenocarcinoma (15.6%), 36 were MIA (46.8%) and 29 were IA (37.7%). IA and MIA/PIL exhibited significant differences in most histogram features and gray-level co-occurrence matrix features (P<0.05). Multivariate logistic regression and ROC curve analyses revealed that smaller energy and higher entropy were significant differentiators of IA from MIA and PIL, irrespective of whether NECT images [area under the curve (AUC): 0.839, 0.859] or CECT images (AUC: 0.818, 0.820) are used. Texture analysis of CT images, regardless of whether NECT or CECT is used, has the potential to distinguish IA from PIL or MIA, particularly the parameters of energy and entropy. Furthermore, NECT images were simpler to obtain and no contrast agent was required; thus, analysis with NECT may be a preferred choice.
本研究旨在探讨基于CT的纹理分析区分表现为磨玻璃密度（GGO）结节的浸润性腺癌（IA）与癌前病变（PIL）或微浸润性腺癌（MIA）的能力，并进一步比较平扫CT（NECT）图像与增强CT（CECT）图像的性能。本项回顾性研究共纳入77例有GGO结节且经手术证实为肺腺癌的患者。每个GGO结节均进行手动分割，并使用在MATLAB（MathWorks）中编写的内部开发软件从NECT和CECT图像中提取其纹理特征。采用独立样本t检验选择IA与MIA/PIL之间具有统计学显著差异的纹理特征。进行多变量逻辑回归和受试者操作特征（ROC）曲线分析以识别预测特征。在77个GGO结节中，12个为非典型腺瘤样增生或腺癌（15.6%），36个为MIA（46.8%），29个为IA（37.7%）。IA与MIA/PIL在大多数直方图特征和灰度共生矩阵特征方面存在显著差异（P<0.05）。多变量逻辑回归和ROC曲线分析显示，无论使用NECT图像[曲线下面积（AUC）：0.839，0.859]还是CECT图像（AUC：0.818，0.820），较小的能量和较高的熵都是IA与MIA和PIL的显著区分因素。CT图像的纹理分析，无论使用NECT还是CECT，都有可能区分IA与PIL或MIA，特别是能量和熵参数。此外，NECT图像获取更简单且无需使用造影剂；因此，使用NECT进行分析可能是首选。
