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Computational modeling in liver surgery

: Christ, B.; Dahmen, U.; Herrmann, K.-H.; König, M.; Reichenbach, J.R.; Ricken, T.; Schleicher, J.; Schwen, L.O.; Vlaic, S.; Waschinsky, N.

Volltext ()

Frontiers in physiology 8 (2017), Art. 906, 26 S.
ISSN: 1664-042X
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer MEVIS ()
Liver resection; risk assessment; systems medicine; multi-scale modeling; function prediction; liver regeneration; liver metabolism; liver surgical planning

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 bytakingintoaccountthespatialrelationshipbetweenthetumorandthehepaticvascular 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 informationoftheindividualpatient.Suchanapproachholdspromiseforbetterprediction 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.