ABSTRACT
PURPOSE: To investigate the feasibility of enhanced computed tomography (CT) radiomics analysis to differentiate between pancreatic cancer (PC) and chronic pancreatitis. METHODS AND MATERIALS: The CT images of 151 PCs and 24 chronic pancreatitis were retrospectively analyzed in the three-dimensional regions of interest on arterial phase (AP) and venous phase (VP) and segmented by MITK software. A multivariable logistic regression model was established based on the selected radiomics features. The radiomics score was calculated, and the nomogram was established. The discrimination of each model was analyzed by the receiver operating characteristic curve (ROC). Decision curve analysis (DCA) was used to evaluate clinical utility. The precision recall curve (PRC) was used to evaluate whether the model is affected by data imbalance. The Delong test was adopted to compare the diagnostic efficiency of each model. RESULTS: Significant differences were observed in the distribution of gender (P = 0.034), carbohydrate antigen 19-9 (P < 0.001), and carcinoembryonic antigen (P < 0.001) in patients with PC and chronic pancreatitis. The area under the ROC curve (AUC) value of AP multivariate regression model, VP multivariate regression model, AP combined with VP features model (Radiomics), clinical feature model, and radiomics combined with clinical feature model (COMB) was 0.905, 0.941, 0.941, 0.822, and 0.980, respectively. The sensitivity and specificity of the COMB model were 0.947 and 0.917, respectively. The results of DCA showed that the COMB model exhibited net clinical benefits and PRC shows that COMB model have good precision and recall (sensitivity). CONCLUSION: The COMB model could be a potential tool to distinguish PC from chronic pancreatitis and aid in clinical decisions.
ABSTRACT
BACKGROUND: Excretory/secretory products (ESPs) released by parasites influence the development and functions of host dendritic cells (DCs). However, little is known about changes of DNA (hydroxy)methylation on DC development during Fasciola gigantica infection. The present study aimed to investigate whether F. gigantica ESPs (FgESPs) affects the development and functions of buffalo DCs through altering the DNA (hydroxy)methylation of DCs. METHODS: Buffalo DCs were prepared from peripheral blood mononuclear cells (PBMCs) and characterized using scanning and transmission electron microscopy (SEM/TEM) and quantitative reverse transcriptional PCR (qRT-RCR). DCs were treated with 200 µg/ml of FgESPs in vitro, following DNA extraction. The DNA methylome and hydroxymethylome were profiled based on (hydroxy)methylated DNA immunoprecipitation sequencing [(h)MeDIP-Seq] and bioinformatics analyses. qRT-RCR was also performed to assess the gene transcription levels of interest. RESULTS: FgESPs markedly suppressed DC maturation evidenced by morphological changes and downregulated gene expression of CD1a and MHC II. Totals of 5432 and 360 genes with significant changes in the 5-methylcytosine (5-mC) and the 5-hydroxymethylcytosine (5-hmC) levels, respectively, were identified in buffalo DCs in response to FgESPs challenge. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that these differentially expressed genes were highly enriched in pathways associated with immune response. Some cancer-related pathways were also indicated. There were 111 genes demonstrating changes in both 5-mC and 5-hmC levels, 12 of which were interconnected and enriched in 12 pathways. The transcription of hypermethylated genes TLR2, TLR4 and IL-12B were downregulated or in a decreasing trend, while the mRNA level of high-hydroxymethylated TNF gene was upregulated in buffalo DCs post-exposure to FgESPs in vitro. CONCLUSIONS: To our knowledge, the present study provides for the first time a unique genome-wide profile of DNA (hydroxy)methylation for DCs that interact with FgESPs, and suggests a possible mechanism of FgESPs in suppressing DC maturation and functions that are involved in TLR signaling.
