Premium
Squaring the circle of selection and allocation in liver transplantation for HCC: An adaptive approach
Author(s) -
Mazzaferro Vincenzo
Publication year - 2016
Publication title -
hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.488
H-Index - 361
eISSN - 1527-3350
pISSN - 0270-9139
DOI - 10.1002/hep.28420
Subject(s) - selection (genetic algorithm) , liver transplantation , transplantation , medicine , computer science , artificial intelligence
Nearly every day, the issue of liver transplantation (LT) for hepatocellular carcinoma (HCC) is debated worldwide during rounds, publications, meetings, and—more importantly—in front of patients with liver cancer who are seeking their doctors’ advice, often after the digital information media have left them and their families empty-handed. Physicians have realized how their certainties can weaken and can strongly differ, regardless of whether the prediction of post-LT outcome is applied to large populations or to single individuals with liver cancer. In addition, liver-dedicated physicians with nontransplantation expertise may find themselves puzzled when dealing with the existing restrictions on the distribution of the scarce resource of donated organs. Allocation rules are in fact continuously released based on adjustments adopted within the transplantation community to maximize patient benefit—defined as an improvement in quantum of life in each patient independent of tumor stage—while avoiding harm to other patients who are waiting for a liver graft. Putting this into practice, the mission of doing justice in transplantation is attempted either through application of the utility principle (i.e., when organs are allocated to patients who have the best post-LT predicted survival) or in adherence to the mandate to care for the “sickest patient first.” In transplantation candidates with HCC, the main obstacle to a smooth organ allocation is the lack of instruments able to determine, with sufficient detail, exactly how sick a patient is, how specific a given tumor presentation is, and how likely the tumor response to various treatments will be. Scores modulated on HCC characteristics have been proposed, but the estimation of the risk of pretransplantation dropout or posttransplantation benefit remains suboptimal. What is missing to fully accomplish the “nearly impossible mission” to frame the complex scenario of