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Background Liver stiffness measurement (LSM) has recently become available for assessment of liver fibrosis. We aimed to develop a prediction model for liver fibrosis using clinical variables, including LSM. Methods We performed a prospective study to compare liver fibrosis grade with fibrosis score. LSM was measured using magnetic resonance elastography in 184 patients that underwent liver resection, and liver fibrosis grade was diagnosed histologically after surgery. Using the prediction model established in the training group, we validated the classification accuracy in the independent test group. Results First, we determined a cut-off value for stratifying fibrosis grade using LSM in 122 patients in the training group, and correctly diagnosed fibrosis grades of 62 patients in the test group with a total accuracy of 69.3%. Next, on least absolute shrinkage and selection operator analysis in the training group, LSM (r = 0.687, P

Background In liver resection, bile leakage remains the most common cause of operative morbidity. In order to predict the risk of this complication on the basis of various factors, we developed a clinical score system to predict the potential risk of bile leakage after liver resection. Methods We analyzed the postoperative course in 518 patients who underwent liver resection for malignancy to identify independent predictors of bile leakage, which was defined as “a drain fluid bilirubin concentration at least three times the serum bilirubin concentration on or after postoperative day 3,” as proposed by the International Study Group of Liver Surgery. To confirm the robustness of the risk score system for bile leakage, we analyzed the independent series of 289 patients undergoing liver resection for malignancy. Results Among 81 (15.6 %) patients with bile leakage, 76 had grade A bile leakage, and five had grade C leakage and underwent reoperation. The median postoperative hospital stay was significantly longer in patients with bile leakage (median, 14 days; range, 8 to 34) than in those without bile leakage (11 days; 5 to 62; P = 0.001). There was no hepatic insufficiency or in-hospital death. The risk score model was based on the four independent predictors of postoperative bile leakage: non-anatomical resection (odds ratio, 3.16; 95 % confidence interval [CI], 1.72 to 6.07; P

Background In the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments. These methods commonly use a list of genes as an analysis input, and most of them produce a more complicated list of genes or pathways as the results of the analysis. Although there are several network-based methods, which detect key nodes in the network, the results tend to include well-studied, major hub genes. Results To mine the molecules that have biological meaning but to fewer degrees than major hubs, we propose, in this study, a new network-based method for selecting these hidden key molecules based on virtual information flows circulating among the input list of genes. The human biomolecular network was constructed from the Pathway Commons database, and a calculation method based on betweenness centrality was newly developed. We validated the method with the ErbB pathway and applied it to practical cancer research data. We were able to confirm that the output genes, despite having fewer edges than major hubs, have biological meanings that were able to be invoked by the input list of genes. Conclusions The developed method, named NetHiKe (Network-based Hidden Key molecule miner), was able to detect potential key molecules by utilizing the human biomolecular network as a knowledge base. Thus, it is hoped that this method will enhance the progress of biological data analysis in the whole-genome research era.