PURPOSE: A model-based risk analysis for oncologic liver surgery was described in previous work (Preim et al. in Proceedings of international symposium on computer assisted radiology and surgery (CARS), Elsevier, Amsterdam, pp. 353–358, 2002; Hansen et al. Int I Comput Assist Radiol Surg 4(5):469–474, 2009). In this paper, we present an evaluation of this method. METHODS: To prove whether and how the risk analysis facilitates the process of liver surgery planning, an explorative user study with 10 liver experts was conducted. The purpose was to compare and analyze their decision-making. RESULTS: The results of the study show that model-based risk analysis enhances the awareness of surgical risk in the planning stage. Participants preferred smaller resection volumes and agreed more on the safety margins’ width in case the risk analysis was available. In addition, time to complete the planning task and confidence of participants were not increased when using the risk analysis. CONCLUSION: This work shows that the applied model-based risk analysis may influence important planning decisions in liver surgery. It lays a basis for further clinical evaluations and points out important fields for future research.
PURPOSE: A model-based risk analysis for oncologic liver surgery was described in previous work (Preim et al. in Proceedings of international symposium on computer assisted radiology and surgery (CARS), Elsevier, Amsterdam, pp. 353–358, 2002; Hansen et al. Int I Comput Assist Radiol Surg 4(5):469–474, 2009). In this paper, we present an evaluation of this method. METHODS: To prove whether and how the risk analysis facilitates the process of liver surgery planning, an explorative user study with 10 liver experts was conducted. The purpose was to compare and analyze their decision-making. RESULTS: The results of the study show that model-based risk analysis enhances the awareness of surgical risk in the planning stage. Participants preferred smaller resection volumes and agreed more on the safety margins’ width in case the risk analysis was available. In addition, time to complete the planning task and confidence of participants were not increased when using the risk analysis. CONCLUSION: This work shows that the applied model-based risk analysis may influence important planning decisions in liver surgery. It lays a basis for further clinical evaluations and points out important fields for future research.
Authors: Johannes Schwaiger; Mathias Markert; Bernhard Seidl; Nikita Shevchenko; Nikolas Doerfler; Tim C Lueth Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2010
Authors: W Lamadé; G Glombitza; L Fischer; P Chiu; C E Cárdenas; M Thorn; H P Meinzer; L Grenacher; H Bauer; T Lehnert; C Herfarth Journal: Arch Surg Date: 2000-11
Authors: Hauke Lang; Arnold Radtke; Milo Hindennach; Tobias Schroeder; Nils R Frühauf; Massimo Malagó; Holger Bourquain; Heinz-Otto Peitgen; Karl J Oldhafer; Christoph E Broelsch Journal: Arch Surg Date: 2005-07
Authors: Miroslav Jiřík; Zbyněk Tonar; Anna Králíčková; Lada Eberlová; Hynek Mírka; Petra Kochová; Tomáš Gregor; Petr Hošek; Miroslava Svobodová; Eduard Rohan; Milena Králíčková; Václav Liška Journal: Int J Comput Assist Radiol Surg Date: 2016-03-23 Impact factor: 2.924
Authors: Bruno Christ; Uta Dahmen; Karl-Heinz Herrmann; Matthias König; Jürgen R Reichenbach; Tim Ricken; Jana Schleicher; Lars Ole Schwen; Sebastian Vlaic; Navina Waschinsky Journal: Front Physiol Date: 2017-11-14 Impact factor: 4.566