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2017-11-14Zeitschriftenartikel DOI: 10.3389/fphys.2017.00906
Computational Modeling in Liver Surgery
Christ, Bruno
Dahmen, Uta cc
Herrmann, Karl-Heinz
König, Matthias cc
Reichenbach, Jürgen R.
Ricken, Tim cc
Schleicher, Jana cc
Schwen, Lars Ole cc
Vlaic, Sebastian
Waschinsky, Navina
Lebenswissenschaftliche Fakultät
The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.
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DOI
10.3389/fphys.2017.00906
Permanent URL
https://doi.org/10.3389/fphys.2017.00906
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<a href="https://doi.org/10.3389/fphys.2017.00906">https://doi.org/10.3389/fphys.2017.00906</a>