• 2018-07
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  • At present there have been


    At present, there have been relatively few studies that evaluated potential correlations between pulmonary and pleura metastases, hilar and mediastinal jak stat nodes, and organs associated with distant metastasis and EGFR mutations [33,34,37]. Further research on these relationships is needed, which may help to identify the similarities and differences between blood and lymphatic metastases and between wild-type EGFR and EGFR + NSCLC, which may help guide diagnosis and treatment. In our study, none of the other primary tumor CT-features (location, lobe, size, density, calcifications, cavitation, vacuole sign, and air bronchogram) significantly associated with EGFR mutations based on multivariate analysis. Several published studies of the relationship between primary tumor size and EGFR mutations yielded inconsistent results; some studies demonstrated a significant association between EGFR mutations and smaller primary tumors [6,11,12,33,38], whereas other studies did not reach the same conclusion [13,19,39]. One study showed a significant correlation between EGFR mutations and larger tumors [40]. Currently, GGO is the most extensively studied CT-feature of tumor density, in terms of identifying correlations with EGFR mutations, but the results are controversial. Many retrospective studies have reported that GGO, a GGO ratio ≥50%, or higher GGO volume percentages were more frequent in tumors with EGFR mutations [6,7,38,41], but some studies revealed no significant associations between GGO and EGFR mutations [11,12]. Additionally, across these same studies, inconsistent calcifications, cavitation, and air bronchogram results were reported [[9], [10], [11], [12], [13]]. These differences may have been due to selection bias resulting from the heterogenous pathologic profiles of the patients, different patient stages, different research parameters, and different statistical methods. In addition, the CT features of EGFR+ pulmonary adenocarcinoma illustrated in this study can be exploited for pattern recognition by artificial intelligence (AI) systems that rely on big data and deep learning and also provide a basis for using AI systems to predict EGFR mutations based on CT images of advanced pulmonary adenocarcinoma. Although associations between CT-based radiomics or deep learning and mutations have been previously explored, most studies focused on early-stage or all stages of NSCLC and on primary tumors [14,42,43].This study demonstrated that emphysema, degree of primary tumor lobulation, and lymph node size and status were significantly associated with EGFR mutation status in cases of advanced pulmonary adenocarcinoma. This suggests that when using deep learning or radiomics to predict EGFR mutation based on CT images of advanced pulmonary adenocarcinoma patients, we should not only focus on the primary tumor but also on lymph nodes and emphysema, which may improve the accuracy of diagnosis. At the same time, the significance of assessing the degree of primary tumor lobulation also puts forward higher requirements for the accurate detection and segmentation of images by an AI system. Huang et al. reported that interobserver variability of tumor contouring affects the use of radiomics for predicting EGFR mutation status [43], which was similar to our findings.
    Introduction Angiotensin II (AngII) is the major bioactive peptide of the Renin-Angiotensin System (RAS) and influences a broad range of homeostatic and modulatory processes, including cardiovascular and renal physiology. Dysregulation of the RAS is associated with disease via actions on cardiac [1], [2], [3], [4], vascular [5], [6] and renal [7], [8] growth and remodeling, modulation of sympathetic nervous system activity [9], [10], [11], endothelial dysfunction [12], angiogenesis [13], [14] and inflammation [15], [16], [17]. AngII acts primarily through the type 1 AngII receptor (AT1R), a G protein-coupled receptor (GPCR), to mediate an array of intracellular signals, including calcium mobilization and generation of reactive oxygen species (ROS), modulation of receptor and non-receptor tyrosine kinases, mitogen-activated protein kinases (MAPK) (including the extracellular-regulated kinase 1/2 (ERK1/2)) and various ion channels [18], [19]. We and others have demonstrated that the AT1R can transactivate the epidermal growth factor receptor (EGFR), which in turn modulates cellular growth, tissue remodeling and cellular hypertrophy [20], [21], [22], [23].