The purpose of this study is to predict preoperatively microvascular invasion (MVI) of solitary small hepatocellular cancer (sHCC) by using the kinetic parameters analysis on dynamic enhancement magnetic resonance imaging (MRI). Patients (n = 61) with known solitary sHCC(≤3cm) were preoperatively examined with Gd-EOB-DTPA-enhanced MRI first before hepatic resection. The arterial peritumoral enhancement measured from the dynamic enhancement-MRI was analyzed by using quantitative kinetic parameters, including initial enhancement (E1), peak enhancement (E''peak''), and enhancement ratio (E''R'') calculated. Correlations between quantitative kinetic parameters and MVI were evaluated and differences between MVI positive and negative groups were assessed. Histopathological analysis of liver resection confirmed that 19 patients had sHCC with MVI and that 42 patients had sHCC without MVI. Average (± standard deviation) E1 is 0.36±0.12 and 0.46±0.09, E''peak'' is 0.78±0.24 and 0.74±0.18, and E''R'' is 0.42±0.20 and 0.56±0.17 for negative and positive group, respectively. Statistical analysis showed that average E1 and ER for the positive group were significantly higher (p less than 0.05) than the negative group. The receiver operating characteristics (ROC) analysis between the two groups had area under the curve of 0.74 and 0.71 for E1 and E''R'', respectively. Quantitative kinetic parameters analysis for the arterial peritumoral enhancement is feasibility to the prediction and assist diagnosis of MVI in clinical practice.
Abstract The purpose of this study is to predict preoperatively microvascular invasion (MVI) of solitary small hepatocellular cancer (sHCC) by using the kinetic parameters analysis [...]
Social network has become the main platform for people to obtain information and connect with each other. Matching user accounts can help us build better users’ profiles and benefit many applications. It has attracted much attention from both industry and academia. At present, cross-platform user identification can be divided into three categories: based on user basic attribute information, user online social structure relationship and user behavior. Research on mobile social networks is a kind of dynamic mixed data analysis. Due to the remarkable heterogeneity of its data across platforms and the incomplete and untrue information caused by users’ concealment behavior, the recognition rate of the algorithm is relatively low. The paper provides a new matching user accounts method based on location verification. First, the self-centered network algorithm is applied to find cross-network edges in the respective networks of the two users to be matched, which is taken as the initial similarity value of the two users. Secondly, the longitude, latitude and time coordinates of a single platform node were used to modify the similarity. Specific 5 time points were selected within 24 hours and the error range of 10min was taken as the calculation method of great circle distance. Thirdly, since the user did not log in a certain social platform in a certain period of time, the convolutional neural network algorithm was adopted to mark the trajectory. Finally, all users in the whole network are identified by iterative operation. Experimental results on artificial random networks and real social networks show that the proposed algorithm has a high readiness rate and recall rate.
Abstract Social network has become the main platform for people to obtain information and connect with each other. Matching user accounts can help us build better users’ profiles [...]
Quantitative analysis of microvascular invasion preoperative prediction in solitary small hepatocellular carcinoma on dynamic enhancement MRI 
