The Impact of the use of C-Arm Cone-Beam CT during Chemoembolization for Hepatocellular Carcinoma

Objective: The objective of the study was to investigate the consequences of using C-arm cone-beam computed tomography (CBCT) on super-selective catheterization of hepatic artery (HA) branches during chemoembolization of hepatocellular carcinoma. Methods: Two groups of patients were created according to the dates of their treatment sessions. Group A and Group B included patients who had their treatment sessions in 2004 - 2005 and 2008 - 2010, respectively. The sessions performed in 2006 and 2007 were excluded to allow for the adoption and incorporation of CBCT imaging into clinical practice. All chemoembolized HA branches were categorized according to selection order (0-1, 2, or ≥3). Other procedure variables were documented. Results: A total of 58 and 183 sessions were included in Groups A and B, respectively, for 144 patients. C-arm CBCT was used in 2 (3%) sessions and 142 (78%) sessions in groups A and B, respectively. The average number of vessels treated was significantly higher in group B (1.8) compared to group A (1.3) (P < .0001). A shift to an increased selection order in group B (0-1, 44 [24%]; 2, 85 [46%]; ≥3, 54 [30%]) was more significant (P = .0004) than that in group A (0-1, 32 [55%]; 2, 18 [31%]; ≥3, 8 [14%]). The average duration of the procedure was significantly longer in group B (P = .0002). Conclusions: Using C-arm CBCT during chemoembolization has a positive impact on increasing the number and order of HA selected and chemoembolized. This comes at the expense of an increase in the duration of the procedure.

Survival impact of C-Arm cone-beam computed tomography on hepatocellular carcinoma patients undergoing chemoembolization

Objectives: The objectives of the study were to evaluate the use of C-arm cone-beam computed tomography (CBCT) for tumor targeting for transarterial chemoembolization (TACE) and its impact on overall survival (OS) in hepatocellular carcinoma patients. Material and Methods: Two groups were retrospectively evaluated according to the date of the first TACE session before and after C-arm CBCT installation in late 2005 (group A [n = 34], 2004–2005; group B [n = 104], 2008+). The years 2006 and 2007 were excluded to allow for the incorporation of this new imaging technology into clinical practice. The vessel selection order was recorded for all TACE sessions. Univariate and multivariate analyses were performed to assess the impact on and predictors of survival. Results: The average TACE selection order for each patient was significantly higher in group B than in group A (P < 0.0001). The median OS was significantly longer in group B (29.34 months) than in group A (19.65 months; P = 0.0088), and the difference in duration was most pronounced in patients with tumor burdens < 25% (n = 93; P = 0.0075), in whom the 3-year survival rate was 56.1% in group B and 15.3% in group A. In these 93 patients, the OS was significant longer (P = 0.018) for high (41.07 months) versus low (19.65 months) vessel selection order across both groups. In multivariate analyses, both the period in which TACE was performed (P = 0.022) and the use of C-arm CBCT (P = 0.0075) were significant predictors of improved OS. Conclusion: Use of advanced C-arm CBCT during TACE enhances the operating physician’s ability to deliver targeted, effective therapy for hepatocellular carcinoma, an aggressive approach that favorably impacts survival.

Research on Optimized Problem-Solving Solutions Selection of the Production Process

In manufacturing industries, various problems may occur during the production process. A problem is a complex status which involves relevant context in working environments. A problem-solving process is often initiated to create a solution for achieving the desired status; in this process, determining how to obtain a solution from the various candidate solutions is an important issue. In such uncertain working environments, context information provides rich clues for problem-solving decision making. Therefore, this work uses a selection approach for an optimized problem-solving process to assist workers in choosing a reasonable solution. A context-based utility model explores the problem context information to obtain the candidate solutions’ actual utility values; a multi-criteria decision analysis uses the actual utility values to determine the optimal selection order of candidate solutions. The selection order is presented to the worker as an adaptive knowledge recommendation. The worker chooses a reasonable problem-solving solution based on the selection order. This paper uses a high-tech company’s knowledge base log as the analysis data. The experimental results show that the chosen approach to an optimized problem-solving solution selection is effective. The contribution of this research is in demonstrating a method which is easy to implement in a problem-solving knowledge recommendation system for selecting a reasonable solution.

A Selection Approach for Optimized Problem-Solving Process by Grey Relational Utility Model and Multicriteria Decision Analysis

In business enterprises, especially the manufacturing industry, various problem situations may occur during the production process. A situation denotes an evaluation point to determine the status of a production process. A problem may occur if there is a discrepancy between the actual situation and the desired one. Thus, a problem-solving process is often initiated to achieve the desired situation. In the process, how to determine an action need to be taken to resolve the situation becomes an important issue. Therefore, this work uses a selection approach for optimized problem-solving process to assist workers in taking a reasonable action. A grey relational utility model and a multicriteria decision analysis are used to determine the optimal selection order of candidate actions. The selection order is presented to the worker as an adaptive recommended solution. The worker chooses a reasonable problem-solving action based on the selection order. This work uses a high-tech company’s knowledge base log as the analysis data. Experimental results demonstrate that the proposed selection approach is effective.