Characterizing different class of patients based on their liver regeneration capacity post hepatectomy and the prediction of safe future liver volume for improved recovery

TLDR: This study fine-tuned the model to predict the liver regeneration in human time-scale by identifying the different response modes post resection as normal recovery, suppressed recovery and liver failure class by generating a cohort of virtual patients using Sobol sampling.

Abstract: The regeneration capacity of liver is used in various clinical interventions from live liver transplant to liver disease such as hepatocellular carcinoma. But the regenerative capacity varies among patients based on various factors such as age, body weights or existing liver disease etc. This study is focused on computational modeling of liver regeneration that integrates signaling mechanisms and cellular functional state transitions. Most of the modeling work on liver regeneration in literature is based on rodent data. We fine-tuned the model to predict the liver regeneration in human time-scale. We categorized the different response modes post resection as normal recovery, suppressed recovery and liver failure class by generating a cohort of virtual patients using Sobol sampling. We then emphasized on the mechanism that distinguishes the response of normal recovery with that of liver failure. We recognized that the net death of hepatocytes and remnant liver mass post hepatectomy are the intrinsic and extrinsic controlling factors respectively, that determines the response of recovery or failure.