Figure 4.

Vision of future liver surgical planning tools. Surgical planning tools of the future will improve risk prediction by accounting for the functional heterogeneity of the healthy and diseased liver and by providing predictions of the functional capacity of the future remnant liver. Multi-scale computational models of the liver will provide the required in silico prediction of function and regeneration (blue box). Key information for surgical planning are time-resolved functional recovery curves, e.g., how clearance of certain substances is affected and recovers after resection. Suitable computational models have to be integrated and validated based on animal models and clinical data (for an overview over computational models of the liver applicable in the context of surgical planning see Tables 1–3). The input data for such function-based risk assessment includes in addition to the assessment of liver geometry, also the spatially resolved assessment of hepatic perfusion and hepatic function as well as clinical data, e.g., quantitative dynamical liver function tests, and information about existing liver disease. Additional output of the future surgical planning tool includes prediction of selected functions after resection, (e.g., hepatic perfusion, metabolic parameters) and their recovery in respect to variation of resection surface and safety margins. CT image stack adapted from (Figure 1B in Chung et al., 2013), image license: CC-BY (https://creativecommons.org/licenses/by/3.0/).