The Potential of Artificial Intelligence to Improve Selection Criteria for Liver Transplantation in HCC
Survival in hepatocellular carcinoma (HCC) remains poor despite advances in therapy. Liver transplantation (LT) offers the best curative option, as it removes both the tumor and the underlying liver disease. However, due to the shortage of donor organs, patient selection and oncologic prognosis are crucial for fair organ allocation. While macrovascular invasion and extrahepatic spread are established contraindications, microvascular invasion and poor tumor differentiation have also emerged as negative prognostic factors. Current LT selection criteria mainly rely on simple imaging parameters such as tumor size and number, neglecting deeper imaging features. Recent studies have highlighted the potential of artificial intelligence (AI) and deep-learning methods for detecting aggressive tumor characteristics like microvascular invasion or poor differentiation—offering a “virtual biopsy.” Moreover, assessing tumor response to transarterial chemoembolization (TACE) may help predict post-transplant survival. This review summarizes diagnostic innovations and their potential impact on organ allocation in HCC.
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