Transplantation Direct (Feb 2022)

Liver Transplantation for Hepatic Adenoma: A UNOS Database Analysis and Systematic Review of the Literature

  • Ioannis A. Ziogas, MD,
  • Panagiotis T. Tasoudis, MS,
  • Nikolaos Serifis, MD,
  • Sophoclis P. Alexopoulos, MD,
  • Martin I. Montenovo, MD,
  • Alexandra Shingina, MD

DOI
https://doi.org/10.1097/TXD.0000000000001264
Journal volume & issue
Vol. 8, no. 2
p. e1264

Abstract

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Background. Liver transplantation (LT) has been employed for hepatic adenoma (HA) on a case-oriented basis. We aimed to describe the characteristics, waitlist, and post-LT outcomes of patients requiring LT for HA. Methods. All patients listed or transplanted for HA in the United States were identified in the United Network for Organ Sharing (UNOS) database (1987–2020). A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis statement. Results. A total of 199 HA patients were listed for LT in UNOS and the crude waitlist mortality was 9.0%. A total of 142 HA patients underwent LT; 118 of these were among those listed with an indication of HA who underwent LT, and 24 were diagnosed incidentally. Most did not experience hepatocellular carcinoma transformation (89.4%). Over a median follow-up of 62.9 mo, death was reported in 18.3%. The 1-, 3-, and 5-y patient survival rates were 94.2%, 89.7%, and 86.3% in the UNOS cohort. The systematic review yielded 61 articles reporting on 99 nonoverlapping patients undergoing LT for HA and 2 articles reporting on multicenter studies. The most common LT indications were suspected malignancy (39.7%), unresectable HA (31.7%), and increasing size (27.0%), whereas 53.1% had glycogen storage disease. Over a median follow-up of 36.5 mo, death was reported in 6.0% (n=5/84). The 1-, 3-, and 5-y patient survival rates were all 95.0% in the systematic review. Conclusions. LT for HA can lead to excellent long-term outcomes in well-selected patients. Prospective granular data are needed to develop more optimal selection criteria and further improve outcomes.