ObjectivesTo systematically review the correlation between NFKB1 gene, NFKBIA gene and lung cancer susceptibility.MethodsWeb of Science, PubMed, VIP, CNKI and WanFang Data databases were electronically searched to collect case-control studies on the correlation between NFKB1 gene rs4648127, rs28362491 polymorphisms and NFKBIA gene rs696 polymorphism and lung cancer susceptibility from inception to November, 2018. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed by using Stata 12.0 software.ResultsA total of 7 case-control studies were included. The results of meta-analysis showed that: no correlation was found between rs4648127 and lung cancer susceptibility (C vs. T: OR=1.065, 95%CI 0.323 to 3.512, P=0.918). A positive correlation was found in hospital population between rs28362491 (D vs. I: OR=1.290, 95%CI 1.117 to 1.489, P=0.001; DD vs. II: OR=1.707, 95%CI 1.273 to 2.289, P<0.001; DD vs. ID+II: OR=1.409, 95%CI 1.100 to 1.806, P=0.008) and lung cancer. Rs696 polymorphism (A vs. G: OR=1.215, 95%CI 1.105 to 1.336, P<0.001; AA vs. GG: OR=1.438, 95%CI 1.194 to 1.731, P<0.001; GG vs. AG+AA: OR=1.566, 95%CI 1.341 to 1.829, P<0.001) was correlated with lung cancer susceptibility.ConclusionsCurrent evidence shows that NFKB1 gene rs4648127 may not be associated with lung cancer. The rs28362491 pdymorphism of NFKB1 gene in hospital population and rs696 pdymorphism of NFKBIA gene may be positively correlated with lung cancer susceptibility. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above conclusions.
Autoimmune pancreatitis (AIP) is a unique subtype of chronic pancreatitis, which shares many clinical presentations with pancreatic ductal adenocarcinoma (PDA). The misdiagnosis of AIP often leads to unnecessary pancreatic resection. 18F-FDG positron emission tomography/ computed tomography (PET/CT) could provide comprehensive information on the morphology, density, and functional metabolism of the pancreas at the same time. It has been proved to be a promising modality for noninvasive differentiation between AIP and PDA. However, there is a lack of clinical analysis of PET/CT image texture features. Difficulty still remains in differentiating AIP and PDA based on commonly used diagnostic methods. Therefore, this paper studied the differentiation of AIP and PDA based on multi-modality texture features. We utilized multiple feature extraction algorithms to extract the texture features from CT and PET images at first. Then, the Fisher criterion and sequence forward floating selection algorithm (SFFS) combined with support vector machine (SVM) was employed to select the optimal multi-modality feature subset. Finally, the SVM classifier was used to differentiate AIP from PDA. The results prove that texture analysis of lesions helps to achieve accurate differentiation of AIP and PDA.