Predicting liver regeneration following major resection
Breakdown of synthesis, excretion and detoxification defines liver failure. Post-hepatectomy liver failure (PHLF) is specific for liver resection and a rightfully feared complication due to high lethality and limited therapeutic success.
Breakdown of synthesis, excretion and detoxification defines liver failure. Post-hepatectomy liver failure (PHLF) is specific for liver resection and a rightfully feared complication due to high lethality and limited therapeutic success. Individual cytokine and growth factor profiles may represent potent predictive markers for recovery of liver function. Worldwide, about 1.4 million patients undergo a liver resection every year. A growing subgroup of patients requires extended surgical procedures to achieve complete tumor clearance. Patients would benefit from a prediction of their individual liver regenerative capacity. Emerging evidence suggests that signalling pathway markers can be used as individual signatures. The association of cytokines and growth factors with liver regeneration potency is widely studied in animal models but clinical data are scarce.
Therefore, they use a pragmatic approach by generating a time-dependent dataset of regeneration regulating cytokines and growth factor profiles based on liquid-biopsy samples from patients undergoing major liver resection. By consideration of this data along with pre- and perioperative clinical information and combination with three different mathematical modelling approaches, they generated an easily assessable, non-invasive, and inexpensive test option reliably predicting individual liver function recovery potency.
In conclusion, they found changes in circulating cytokines and growth factors of patients undergoing major liver resection that are clearly linked to the postoperative morbidity and mortality. With the demonstrated liquid biopsy-based approach they are able to predict potentially fatal postoperative clinical outcomes and the individual regenerative potential. This approach may allow a risk-adapted selection of patients.
https://www.nature.com/articles/s41598-022-16968-9
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