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Christ B, Dahmen U, Herrmann KH, et al. Computational Modeling in Liver Surgery. Front Physiol. 2017;8:906doi: 10.3389/fphys.2017.00906.
Christ, B., Dahmen, U., Herrmann, K. H., König, M., Reichenbach, J. R., Ricken, T., Schleicher, J., Ole Schwen, L., Vlaic, S., & Waschinsky, N. (2017). Computational Modeling in Liver Surgery. Frontiers in physiology, 8906. https://doi.org/10.3389/fphys.2017.00906
Christ, Bruno, et al. "Computational Modeling in Liver Surgery." Frontiers in physiology vol. 8 (2017): 906. doi: https://doi.org/10.3389/fphys.2017.00906
Christ B, Dahmen U, Herrmann KH, König M, Reichenbach JR, Ricken T, Schleicher J, Ole Schwen L, Vlaic S, Waschinsky N. Computational Modeling in Liver Surgery. Front Physiol. 2017 Nov 14;8:906. doi: 10.3389/fphys.2017.00906. eCollection 2017. PMID: 29249974; PMCID: PMC5715340.
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Front Physiol. 2017 Nov 14;8:906. doi: 10.3389/fphys.2017.00906. eCollection 2017.

Computational Modeling in Liver Surgery.

Frontiers in physiology

Bruno Christ, Uta Dahmen, Karl-Heinz Herrmann, Matthias König, Jürgen R Reichenbach, Tim Ricken, Jana Schleicher, Lars Ole Schwen, Sebastian Vlaic, Navina Waschinsky

Affiliations

  1. Molecular Hepatology Lab, Clinics of Visceral, Transplantation, Thoracic and Vascular Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany.
  2. Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany.
  3. Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany.
  4. Department of Biology, Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany.
  5. Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany.
  6. Department of Bioinformatics, Friedrich Schiller University Jena, Jena, Germany.
  7. Fraunhofer MEVIS, Bremen, Germany.
  8. Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany.

PMID: 29249974 PMCID: PMC5715340 DOI: 10.3389/fphys.2017.00906

Abstract

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.

Keywords: Liver resection; function prediction; liver metabolism; liver regeneration; liver surgical planning; multi-scale modeling; risk assessment; systems medicine

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