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Computational Modeling in Liver Surgery.

Bruno Christ1, Uta Dahmen2, Karl-Heinz Herrmann3

  • 1Molecular Hepatology Lab, Clinics of Visceral, Transplantation, Thoracic and Vascular Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany.

Frontiers in Physiology
|December 19, 2017
PubMed
Summary
This summary is machine-generated.

Extended liver resections are rising. Integrating anatomical and functional data with systems medicine models can improve surgical planning and predict liver function, reducing risks like liver failure.

Keywords:
Liver resectionfunction predictionliver metabolismliver regenerationliver surgical planningmulti-scale modelingrisk assessmentsystems medicine

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Area of Science:

  • Hepatology
  • Surgical Oncology
  • Computational Biology

Background:

  • Increasing incidence of liver tumors necessitates extended liver resections.
  • Current computational tools for virtual liver resection planning focus on volume but may misestimate function, especially with comorbidities like hepatic steatosis.
  • Accurate prediction of postoperative liver function is crucial for minimizing risks.

Purpose of the Study:

  • To review mathematical models of liver function and their relevance for computational liver surgery.
  • To explore advanced computational planning tools for improved surgical risk assessment.
  • To integrate biological, medical, and surgical aspects for personalized liver surgery.

Main Methods:

  • Review of hepatic anatomy, physiology, and pathology relevant to surgery.
  • Description of current computational tools used in liver surgical planning.
  • Presentation of state-of-the-art computational liver models.

Main Results:

  • Current virtual planning tools primarily consider liver volume, potentially oversimplifying function prediction.
  • Mathematical models offer a pathway to integrate diverse patient data for more accurate functional assessment.
  • A systems medicine approach promises enhanced prediction of postoperative liver function.

Conclusions:

  • Advanced computational models are needed to accurately predict postoperative liver function beyond mere volume calculations.
  • Integrating functional data into surgical planning is key for managing patients undergoing extended liver resections.
  • Systems medicine approaches hold significant potential for improving outcomes in liver surgery.