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Abstract Details
Hepatocellular Carcinoma, Alpha Fetoprotein, and Liver Allocation for Transplantation: Past, Present and Future
Curr Oncol. 2022 Oct 8;29(10):7537-7551. doi: 10.3390/curroncol29100593.
1Department of Surgery, Division of Transplant Surgery, Mayo Clinic, Phoenix, AZ 85054, USA.
2Department of Surgery, Mayo Clinic Arizona, Phoenix, AZ 85054, USA.
3Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, USA.
4Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
5Department of Surgery, Division of Solid Organ Transplant, University of Arkansas for Medical Sciences Medical Center, Little Rock, AR 72205, USA.
Abstract
Hepatocellular carcinoma (HCC) is one of the leading indications for liver transplantation and has been the treatment of choice due to the oncologic benefit for patients with advanced chronic liver disease (AdvCLD) and small tumors for the last 25 years. For HCC patients undergoing liver transplantation, alpha fetoprotein (AFP) has increasingly been applied as an independent predictor for overall survival, disease free recurrence, and waitlist drop out. In addition to static AFP, newer studies evaluating the AFP dynamic response to downstaging therapy show enhanced prognostication compared to static AFP alone. While AFP has been utilized to select HCC patients for transplant, despite years of allocation policy changes, the US allocation system continues to take a uniform approach to HCC patients, without discriminating between those with favorable or unfavorable tumor biology. We aim to review the history of liver allocation for HCC in the US, the utility of AFP in liver transplantation, the implications of weaving AFP as a biomarker into policy. Based on this review, we encourage the US transplant community to revisit its HCC organ allocation model, to incorporate more precise oncologic principles for patient selection, and to adopt AFP dynamics to better stratify waitlist dropout risk.