Background/Objectives: Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths rising worldwide. This is leading to an increased demand for liver transplantation (LT), the most effective treatment for HCC in its initial stages. However, current patient selection criteria are limited in predicting recurrence and raise ethical concerns about equitable access to care. This study aims to enhance patient selection by refining the HepatoPredict (HP) tool, a machine learning-based model that combines molecular and clinical data to forecast LT outcomes. Methods: The updated HP algorithm was trained on a two-center dataset and assessed against standard clinical criteria. Its prognostic performance was evaluated through accuracy metrics, with additional analyses considering tumor heterogeneity and potential sampling bias. Results: HP outperformed all clinical criteria, particularly regarding negative predictive value, addressing critical limitations in existing selection strategies. It also demonstrated improved differentiation of recurrence-free and overall survival outcomes. Importantly, the prognostic accuracy of HP remained largely unaffected by intra-nodule and intra-patient heterogeneity, indicating its robustness even when biopsies were taken from smaller or non-dominant nodules. Conclusions: These findings support the usage of HP as a valuable tool for optimizing LT candidate selection, promoting fair organ allocation, and enhancing patient outcomes through integrated analysis of molecular and clinical data.

The work described here is subject to patent WO 2021/064230 A1; JBPL, JC, and HPM declare an ownership interest in the company Ophiomics. MM, MCQ, LF, CP, MGR, DP, and AFo are Ophiomics employees.

This work was partly funded by a grant from the European Innovation Council under the EIC Accelerator scheme (Contract N.946364). MB is funded by the Instituto de Salud Carlos III and co-funded by European Regional Development Fund A way to make Europe (PI23/00088 and INT24/00021), by the Generalitat Valenciana (CIPROM/2023/16), by the CIBER -Consorcio Centro de Investigacion Biomedica en Red- [CB06/04/0065], Instituto de Salud Carlos III, Ministerio de Ciencia e Innovacion and Union Europea - European Regional Development Fund, and by the Spanish Society of Liver Transplantation (2022/295).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The retrospective clinical study was approved by the ethics authorities from the Coimbra Hospital (Coimbra, Portugal), the Curry Cabral Hospital (Lisbon, Portugal), and La Fe Hospital (Valencia, Spain).

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The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.