In the growing elderly societies, the incidence of primary and secondary liver tumors is continuously increasing. Surgical interventions like partial liver resections are often the only curative therapy removing more than 70% of the total liver mass. In humans, 20% to 30% of the remaining liver is required for liver regeneration. Successful restoration of the liver mass ultimately depends on the regenerative and metabolic capacity of the hepatocytes in the remnant liver. This, however, might be limited by individual factors like age, preceding chemotherapy, or by metabolic challenges like liver steatosis. The balance between sufficient tissue removal to avoid tumor recurrence and maintaining a minimal functional liver mass for regenerative and metabolic homeostasis today depends on the surgeon´s individual decision. Currently, liver surgery may be supported by computational tools, which take into account the location of the tumor relative to the liver vascular tree estimating volume and perfusion to predict post-surgery liver performance. This approach might misestimate the remnant liver function, because it does not consider functional aspects on the cellular level. These, however, are of ultimate importance to predict the regenerative and metabolic capacity of the remnant liver as a whole. E.g., post-resection lipid accumulation in the hepatocytes is necessary to provide energy substrates for liver regeneration. However, “metabolic overload” after extended resections is often the cause of post-surgery liver failure. Since lipid metabolism in the liver features both regional and zonal heterogeneity, integration of metabolic computational models on the cellular level would clearly support pre-surgical planning.
Current surgical planning tools focus on the estimation of liver volume as a surrogate predictor of remnant liver function. The underlying assumption is that all hepatocytes contribute equally to liver function. This, however, neglects the spatial heterogeneity of liver metabolism and perfusion, potential alterations of hepatic function in the presence of a liver disease, or individual variations in metabolic function due to genetic variants, or as a consequence of lifestyle. A systems medicine approach including these biological, medical, and surgical aspects with available anatomical and functional information of the individual patient holds the promise for better prediction of postoperative liver function and hence improved risk assessment. Regulation and maintenance of liver function involves complex biological processes spanning multiple spatial and temporal scales. Spatial scales range from the intracellular level up to the level of the organism, whereas temporal scales have to reflect time periods of seconds to years (e.g., metabolism in seconds to days, regeneration over weeks, or disease progression over months). Thus, multi-scale-oriented modeling approaches are especially suited to provide a more comprehensive understanding of hepatic processes and mechanisms relevant in the context of hepatic resection. We will present computational models/modeling approaches for addressing liver functions, which might be essential for future multi-scale models supporting liver resection: (a) the hepatic stress response following physical damage, (b) the metabolic pathways affected by surgery, as well as (c) the regeneration of liver volume and function recovery. Our focus lies on selected liver-specific models from the field of systems biology relevant for liver surgery.
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Created: 9th Jul 2018 at 10:23
Last updated: 9th Jul 2018 at 10:24
Last used: 17th Jan 2020 at 18:10