ArticleWorld journal of hepatology2025
Explant-based prognostic models for hepatocellular carcinoma recurrence after liver transplantation: A systematic review and meta-analysis.
Article in World journal of hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLiver transplantation (LT) is the preferred curative treatment for early-stage hepatocellular carcinoma (HCC). However, approximately 17% of patients experience post-transplant recurrence. Extrahepatic recurrence and early recurrence (within one year after LT) are associated with poorer post-recurrence survival.
aimTo assess which explant-based prognostic model best predicts HCC recurrence after LT.
methodsA systematic search was performed in PubMed, EMBASE, Web of Science, and the Cochrane Library from inception to January 30, 2025. Nine retrospective studies comprising 5348 patients were included. Three explant-based prognostic models were analyzed: (1) Risk estimation of tumor recurrence after transplant (RETREAT); (2) Decaens; and (3) Predicting Cancer Recurrence Score (PCRS). Primary outcomes included: (1) HCC recurrence rate; and (2) Predictive accuracy of each score over a five-year follow-up.
resultsAll studies were retrospective and included validation cohorts from North America, Europe, and Asia. The overall recurrence rate was 7%. For high-risk thresholds, pooled sensitivity and specificity were Risk Estimation of Tumor Recurrence after Transplant (RETREAT) ≥ 5 (0.381/0.953), Decaens ≥ 4 (0.676/0.817), and PCRS ≥ 3 (0.217/0.987). Among high-risk patients, recurrence reached 45% (95%CI: 35.1-57.0). Area under the curve comparisons showed no statistically significant differences among models. Thus, no model demonstrated clear superiority.
conclusionAlthough several explant-based models exist, their limited sensitivity suggests that many patients at risk of recurrence remain unidentified. The RETREAT score, developed in a large cohort, remains the most extensively validated. Future approaches should focus on developing improved prognostic tools using larger, preferably prospective datasets, and integrating artificial intelligence to enhance risk stratification and post-transplant surveillance.
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