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

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.

CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: A systematic review

“…In another study, reducing pixel size could improve the model performance in IAC prediction [45]. In this review, one included study [31] showed that radiomics model based on CECT images performed better than that based on NCECT images, which was not found in the other two included studies [33,41]. Segmentation influences the results of the radiomics analysis.…”

“…The number of patients/nodules ranged between 34/34 and 794/886. Eight studies [15,[17][18][19][20][21][22][23] included only mixed ground-glass nodules (MGGNs), seven [24][25][26][27][28][29][30] only pure ground-glass nodules (PGGNs), and thirteen [14,16,[31][32][33][34][35][36][37][38][39][40][41] both of MGGNs and PGGNs.…”

“…Most studies (23/28) performed radiomics analysis based on non-contrast enhanced CT (NCECT) images, only one [18] on contrast enhanced CT (CECT) images. Three studies [31,33,41] compared the model performance between NECT and CECT images. In the study by Fan et al [14], the contrast-enhanced cohort was used as an external validation set.…”

“…The ROIs were segmented manually in 20 studies [14,15,18,21,22,[24][25][26][27]29,[31][32][33][34][36][37][38][39][40][41], semi-automatically in six studies [17,19,20,28,30,35] and automatically in two studies [16,23] (Table 2). Multiple segmentations, more than one person segmented the ROIs, were conducted in 24 studies.…”

“…No identical features set could be identified in any two studies. Entropy was identified as signature in seven studies [23,26,30,36,37,40,41], Volume in three studies [19,23,33], Maximum 3D diameter [29,36] and root mean squared [20,36] in two studies. Three studies [14][15][16] performed the model in a geographical validation set, and four [17,21,31,36] in a temporal validation set.…”