Pulmonary adenocarcinoma appearing as ground‑glass opacity nodules identified using non‑enhanced and contrast‑enhanced CT texture analysis: A retrospective analysis

Ye,Jing; Ling,Jun; Lv,Yan; Chen,Juan; Cai,Junhui; Chen,Mingxiang

doi:10.3892/etm.2020.8511

Pulmonary adenocarcinoma appearing as ground‑glass opacity nodules identified using non‑enhanced and contrast‑enhanced CT texture analysis: A retrospective analysis

Authors:
- Jing Ye
- Jun Ling
- Yan Lv
- Juan Chen
- Junhui Cai
- Mingxiang Chen
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Affiliations: Department of Medical Imaging, Yangzhou University Clinical College Subei People's Hospital, Yangzhou, Jiangsu 225002, P.R. China
Published online on: February 10, 2020 https://doi.org/10.3892/etm.2020.8511
Pages: 2483-2490
Copyright: © Ye et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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 t‑test 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 in situ (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.

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