RET fusion occurs in 1.4% of NSCLCs and 1.7% of lung adenocarcinomas and has identifiable clinicopathologic characteristics, warranting further clinical consideration and targeted therapy investigation.

BackgroundThe clinicopathologic characteristics of tumors expressing programmed death (PD-1) ligands (PD-Ls) PD-L1 or PD-L2 and their associations with common driver mutations in lung adenocarcinoma are not clearly defined, despite the progression of anti-PD-1/PD-L1 immunotherapy.MethodsPD-L1 and PD-L2 expression was measured by immunohistochemistry in 143 surgically resected lung adenocarcinomas and was correlated with clinical variables, histologic subtypes, and the mutational status of EGFR, KRAS, HER2, and ALK.ResultsPositive PD-L1 expression was significantly associated with more advanced T status, N status, and pathologic stage. Histologically, lung adenocarcinomas with positive PD-L1 staining were less likely to be adenocarcinoma in situ or minimally invasive adenocarcinoma and more likely to have solid predominant subtype. Both PD-L1 expression (odds ratio =1.984, 95% confidence interval =1.010–3.894; P=0.047) and PD-L2 expression (odds ratio =2.328, 95% confidence interval =1.201–4.512; P=0.012) were independent predictors of poor overall survival. When the combined PD-L expression and pathologic stage were used together to predict overall survival, the concordance index increased to 0.763, and the Akaike information criteria value decreased to 356.08.ConclusionWe defined the clinicopathologic features of lung adenocarcinomas with high expression of PD-L1 and PD-L2. We further demonstrated the role of PD-L expression as a useful prognostic marker for lung adenocarcinoma.

Adaptive JPEG steganographic schemes are difficult to preserve the image texture features in all scales and orientations when the embedding changes are constrained to the complicated texture regions, then a steganalysis feature extraction method is proposed based on 2 dimensional (2D) Gabor filters. The 2D Gabor filters have certain optimal joint localization properties in the spatial domain and in the spatial frequency domain. They can describe the image texture features from different scales and orientations, therefore the changes of image statistical characteristics caused by steganography embedding can be captured more effectively. For the proposed feature extraction method, the decompressed JPEG image is filtered by 2D Gabor filters with different scales and orientations firstly. Then, the histogram features are extracted from all the filtered images.Lastly, the ensemble classifier is used to assemble the proposed steganalysis feature as well as the final steganalyzer. The experimental results show that the proposed steganalysis feature can achieve a competitive performance by comparing with the other steganalysis features when they are used for the detection performance of adaptive JPEG steganography such as UED, JUNIWARD and SI-UNIWARD.

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