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HepatoPredict Accurately Selects Hepatocellular Carcinoma Patients for Liver Transplantation Regardless of Tumor Heterogeneity.

2025-02-02Text Open access
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dc.contributor.authorAndrade, Rita
dc.contributor.authorPerez-Rojas, Judith
dc.contributor.authorda Silva, Sílvia Gomes
dc.contributor.authorMiskinyte, Migla
dc.contributor.authorQuaresma, Margarida C
dc.contributor.authorFrazão, Laura P
dc.contributor.authorPeixoto, Carolina
dc.contributor.authorCubells, Almudena
dc.contributor.authorMontalvá, Eva M
dc.contributor.authorFigueiredo, António
dc.contributor.authorCipriano, Augusta
dc.contributor.authorGonçalves-Reis, Maria
dc.contributor.authorProença, Daniela
dc.contributor.authorFolgado, André
dc.contributor.authorPereira-Leal, José B
dc.contributor.authorOliveira, Rui Caetano
dc.contributor.authorPinto-Marques, Hugo
dc.contributor.authorTralhão, José Guilherme
dc.contributor.authorBerenguer, Marina
dc.contributor.authorCardoso, Joana
dc.date.accessioned2025-10-21T14:52:17Z
dc.date.available2025-10-21T14:52:17Z
dc.date.issued2025-02-02
dc.description.abstractBackground/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.eng
dc.identifier.citationCancers (Basel) . 2025 Feb 2;17(3):500. doi: 10.3390/cancers17030500.
dc.identifier.doi10.3390/cancers17030500.
dc.identifier.pmid39941867
dc.identifier.urihttp://hdl.handle.net/10400.17/5189
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationContract Nº946364/European Innovation Council EIC Accelerator Scheme
dc.relationPI23/00088 and INT24/00021/European Regional Development Fund 'A way to make Europe'
dc.relationCIPROM/2023/16/Generalitat Valenciana
dc.relationCB06/04/0065/CIBER -Consorcio Centro de Investigación Biomédica en Red
dc.relationN/A/Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación and Unión Europea-European Regional Development Fund
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectHCC CHBPT
dc.subjectHCC ANPAT
dc.subjectHCC
dc.subjectHepatoPredict
dc.subjectLiver Transplant
dc.subjectLiver Biopsy
dc.subjectPrognostic Test
dc.subjectMulti-Target Genomic Assay
dc.subjectTumor Heterogeneity
dc.titleHepatoPredict Accurately Selects Hepatocellular Carcinoma Patients for Liver Transplantation Regardless of Tumor Heterogeneity.eng
dc.typetext
dspace.entity.typePublication
oaire.citation.issue3
oaire.citation.startPage500
oaire.citation.titleCancers
oaire.citation.volume17
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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