In the context of severe liver diseases, the prediction of the treatment outcome mainly relies on observational clinical data reflecting liver function as a whole at best. These, however, are impacted by the individual situation of the patient comprising age, gender, comorbidities, pre-treatment medication, and general physical conditions. In hepatology and gastroenterology disciplines, algorithms have been developed to grade acute liver failure emerging on the ground of chronic diseases like cirrhosis. However, in liver surgery, a reliable risk assessment for postoperative liver failure, a severe complication with often fatal consequences for the patient, is still lacking. Yet, to improve the therapeutic and/or surgical outcome of patients suffering from liver diseases, both in internal medicine and surgery disciplines, concrete guidelines based on reliable clinical data and predictive algorithms are urgently needed.
The liver represents a multiscale network comprising three levels: organ, lobe, and cell level. In addition, hepatic functions are distributed heterogeneously throughout the parenchyma, rendering the hepatocytes surrounding the portal tracts to execute, e.g., ammonia fixation by ureagenesis, and hepatocytes surrounding the hepatic vein to fix ammonia via the glutamine synthetase reaction. Thus, multiscale models describing liver function may unequivocally help to design predictive tools for assessing the surgical risk and the probability of therapeutic success.
Recent developments of computational models make it possible to describe organ functions on different scales. It is the goal of this Research Topic, to collect papers involving cutting-edge approaches of multiscale computational modeling in the context of liver surgery and interventions. We invite authors to send their latest and highly innovative research or comprehensive review articles related to the scope of this Research Topic.
To predict post-treatment liver function before surgical, radiological, or pharmacological interventions is the most challenging task for clinicians when treating severe liver diseases. Based on the individual unique situation of the patient, novel therapy concepts are warranted in order to stratify therapy outcome. In order to open the perspective for the development of predictive tools anticipating liver function post-treatment, we invite submissions entailing computational approaches of liver modeling or methodology for a data-based patient-specific risk assessment in the context of liver surgery and interventions. Research should address, but is not restricted to modeling and data-based analysis of hepatic metabolism, liver perfusion, regeneration, and communication with other organs. Research dealing with multiscale modeling is specifically encouraged. We also greatly appreciate research comprising machine learning/artificial intelligence methods including knowledge-driven approaches and consideration of uncertainty.
In the context of severe liver diseases, the prediction of the treatment outcome mainly relies on observational clinical data reflecting liver function as a whole at best. These, however, are impacted by the individual situation of the patient comprising age, gender, comorbidities, pre-treatment medication, and general physical conditions. In hepatology and gastroenterology disciplines, algorithms have been developed to grade acute liver failure emerging on the ground of chronic diseases like cirrhosis. However, in liver surgery, a reliable risk assessment for postoperative liver failure, a severe complication with often fatal consequences for the patient, is still lacking. Yet, to improve the therapeutic and/or surgical outcome of patients suffering from liver diseases, both in internal medicine and surgery disciplines, concrete guidelines based on reliable clinical data and predictive algorithms are urgently needed.
The liver represents a multiscale network comprising three levels: organ, lobe, and cell level. In addition, hepatic functions are distributed heterogeneously throughout the parenchyma, rendering the hepatocytes surrounding the portal tracts to execute, e.g., ammonia fixation by ureagenesis, and hepatocytes surrounding the hepatic vein to fix ammonia via the glutamine synthetase reaction. Thus, multiscale models describing liver function may unequivocally help to design predictive tools for assessing the surgical risk and the probability of therapeutic success.
Recent developments of computational models make it possible to describe organ functions on different scales. It is the goal of this Research Topic, to collect papers involving cutting-edge approaches of multiscale computational modeling in the context of liver surgery and interventions. We invite authors to send their latest and highly innovative research or comprehensive review articles related to the scope of this Research Topic.
To predict post-treatment liver function before surgical, radiological, or pharmacological interventions is the most challenging task for clinicians when treating severe liver diseases. Based on the individual unique situation of the patient, novel therapy concepts are warranted in order to stratify therapy outcome. In order to open the perspective for the development of predictive tools anticipating liver function post-treatment, we invite submissions entailing computational approaches of liver modeling or methodology for a data-based patient-specific risk assessment in the context of liver surgery and interventions. Research should address, but is not restricted to modeling and data-based analysis of hepatic metabolism, liver perfusion, regeneration, and communication with other organs. Research dealing with multiscale modeling is specifically encouraged. We also greatly appreciate research comprising machine learning/artificial intelligence methods including knowledge-driven approaches and consideration of uncertainty.
