• Hugo Pinto-Marques, Joana Cardoso, Sílvia Silva, João L Neto, Maria Gonçalves-Reis, Daniela Proença, Marta Mesquita, André Manso, Sara Carapeta, Mafalda Sobral, Antonio Figueiredo, Clara Rodrigues, Adelaide Milheiro, Ana Carvalho, Rui Perdigoto, Eduardo Barroso, and José B Pereira-Leal.
• Hepato-Biliary-Pancreatic and Transplantation Centre, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal.
• Ann. Surg. 2022 Nov 1; 276 (5): 868-874.

ObjectiveTo propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT).BackgroundLiver cancer is one of the most frequent causes of cancer-related mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation.MethodsA literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT (>5 years follow up, 32% beyond Milan criteria). The resulting 4 gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict.ResultsHepatoPredict identifies 99% disease-free patients (>5 year) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88.5%-94.4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term.ConclusionsHepatoPredict outperforms conventional clinical-pathologic selection criteria (Milan, UCSF), providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.

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