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Background: Nonalcoholic fatty liver disease (NAFLD) increases the possibility to suffer from liver or cardiovascular disease. Although hepatic biopsy is well acknowledged as the standard diagnosis, it is difficult to implement because of its intrusiveness and cost concerns. Moreover, overweight people or diabetic patients are always NAFLD-positive, but not absolute. Therefore, to distinguish whether a diabetic case has NAFLD via nonintrusive indicators is of great significance for further interventions. Objective: With 8499 diabetic patients hosted by Shanghai Sixth People’s Hospital, we try to rank the impacts of multiple routine indicators (features) on NAFLD, and further predict NAFLD within this diabetic population. Methods: We first rank dozens of related features according to their contributions in NAFLD prediction, and then we prune several trivial features to simplify the prediction. Additionally, three classification algorithms are considered and compared, e.g., C4.5, Naïve Bayes and Random Forest. Results: The experiment shows that Random Forest outperforms the rest (accuracy 85.1%, recall 90.98% and AUC 0.631). Conclusions: We find that the top nine markers together can effectively tell NAFLD out of this diabetic population. They are triglyceride (TG), low density lipoprotein (LDL), insulin (INS), hbA1C, high-density lipoprotein (HDL), fasting plasma glucose (FPG), age, total cholesterol (TC) and duration.

Background Liver cancer is one of the most common malignancies worldwide. HCC (hepatocellular carcinoma) is the predominant pathological type of liver cancer, accounting for approximately 75–85 % of all liver cancers. Lipid metabolic reprogramming has emerged as an important feature of HCC. However, the influence of lipid metabolism-related gene expression in HCC patient prognosis remains unknown. In this study, we performed a comprehensive analysis of HCC gene expression data from TCGA (The Cancer Genome Atlas) to acquire further insight into the role of lipid metabolism-related genes in HCC patient prognosis. Methods We analyzed the mRNA expression profiles of 424 HCC patients from the TCGA database. GSEA(Gene Set Enrichment Analysis) was performed to identify lipid metabolism-related gene sets associated with HCC. We performed univariate Cox regression and LASSO(least absolute shrinkage and selection operator) regression analyses to identify genes with prognostic value and develop a prognostic model, which was tested in a validation cohort. We performed Kaplan-Meier survival and ROC (receiver operating characteristic) analyses to evaluate the performance of the model. Results We identified three lipid metabolism-related genes (ME1, MED10, MED22) with prognostic value in HCC and used them to calculate a risk score for each HCC patient. High-risk HCC patients exhibited a significantly lower survival rate than low-risk patients. Multivariate Cox regression analysis revealed that the 3-gene signature was an independent prognostic factor in HCC. Furthermore, the signature provided a highly accurate prediction of HCC patient prognosis. Conclusions We identified three lipid-metabolism-related genes that are upregulated in HCC tissues and established a 3-gene signature-based risk model that can accurately predict HCC patient prognosis. Our findings support the strong links between lipid metabolism and HCC and may facilitate the development of new metabolism-targeted treatment approaches for HCC.

We designed the image-enabled EHR sharing solution (i-EHR) for cross-enterprise and cross-domain with SOA architecture and combined the grid-based image management and distribution capability, which are compliant with IHE XDS-I/XCA integration profiles. We selected one districts with four hospitals and two hospital groups as image sharing pilot testing bed. Our approach presented in this presentation uses peer-to-peer mode to share and exchange image data cross enterprise PACSs and domains, which provides single point of services to local systems so it is easy to integrate with different vendor's PACS and easy to deploy to different hospitals to implement the i-EHR.

There are many Web-based image accessing technologies used in medical imaging area, such as component-based (ActiveX Control) thick client Web display, Zerofootprint thin client Web viewer (or called server side processing Web viewer), Flash Rich Internet Application(RIA) ,or HTML5 based Web display. Different Web display methods have different peformance in different network environment. In this presenation, we give an evaluation on two developed Web based image display systems. The first one is used for thin client Web display. It works between a PACS Web server with WADO interface and thin client. The PACS Web server provides JPEG format images to HTML pages. The second one is for thick client Web display. It works between a PACS Web server with WADO interface and thick client running in browsers containing ActiveX control, Flash RIA program or HTML5 scripts. The PACS Web server provides native DICOM format images or JPIP stream for theses clients.