A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver

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Background: Liver resection is the mainstay treatment option for patients with hepatocellular carcinoma in the non-cirrhotic liver (NCL-HCC), but almost half of these patients will experience a recurrence within five years of surgery. Therefore, we aimed to develop a rationale-based risk evaluation...

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Detalles Bibliográficos
Autores: Ruiz, E, Honles, J, Fernández, R, Uribe, K, Cerapio, JP, Cancino, K, Contreras-Mancilla, J, Casavilca-Zambrano, S, Berrospi, F, Pineau, P, Bertani, S
Formato: artículo
Fecha de Publicación:2024
Institución:Instituto Nacional de Enfermedades Neoplásicas
Repositorio:INEN-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.inen.sld.pe:20.500.14703/385
Enlace del recurso:https: //doi.org/10.1016/j.hpb.2024.02.010
https://hdl.handle.net/20.500.14703/385
Nivel de acceso:acceso abierto
Materia:Adult
Aged
Carcinoma, Hepatocellular
Female
Hepatectomy
Humans
Liver Neoplasms
Machine Learning
Male
Middle Aged
Neoplasm Recurrence, Local
Retrospective Studies
Risk Assessment
Risk Factors
Time Factors
Treatment Outcome
https://purl.org/pe-repo/ocde/ford#3.02.21
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dc.title.none.fl_str_mv A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
title A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
spellingShingle A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
Ruiz, E
Adult
Aged
Carcinoma, Hepatocellular
Female
Hepatectomy
Humans
Liver Neoplasms
Machine Learning
Male
Middle Aged
Neoplasm Recurrence, Local
Retrospective Studies
Risk Assessment
Risk Factors
Time Factors
Treatment Outcome
https://purl.org/pe-repo/ocde/ford#3.02.21
title_short A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
title_full A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
title_fullStr A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
title_full_unstemmed A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
title_sort A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
author Ruiz, E
author_facet Ruiz, E
Honles, J
Fernández, R
Uribe, K
Cerapio, JP
Cancino, K
Contreras-Mancilla, J
Casavilca-Zambrano, S
Berrospi, F
Pineau, P
Bertani, S
author_role author
author2 Honles, J
Fernández, R
Uribe, K
Cerapio, JP
Cancino, K
Contreras-Mancilla, J
Casavilca-Zambrano, S
Berrospi, F
Pineau, P
Bertani, S
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ruiz, E
Honles, J
Fernández, R
Uribe, K
Cerapio, JP
Cancino, K
Contreras-Mancilla, J
Casavilca-Zambrano, S
Berrospi, F
Pineau, P
Bertani, S
dc.subject.none.fl_str_mv Adult
Aged
Carcinoma, Hepatocellular
Female
Hepatectomy
Humans
Liver Neoplasms
Machine Learning
Male
Middle Aged
Neoplasm Recurrence, Local
Retrospective Studies
Risk Assessment
Risk Factors
Time Factors
Treatment Outcome
topic Adult
Aged
Carcinoma, Hepatocellular
Female
Hepatectomy
Humans
Liver Neoplasms
Machine Learning
Male
Middle Aged
Neoplasm Recurrence, Local
Retrospective Studies
Risk Assessment
Risk Factors
Time Factors
Treatment Outcome
https://purl.org/pe-repo/ocde/ford#3.02.21
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#3.02.21
description Background: Liver resection is the mainstay treatment option for patients with hepatocellular carcinoma in the non-cirrhotic liver (NCL-HCC), but almost half of these patients will experience a recurrence within five years of surgery. Therefore, we aimed to develop a rationale-based risk evaluation tool to assist surgeons in recurrence-related treatment planning for NCL-HCC. Methods: We analyzed single-center data from 263 patients who underwent liver resection for NCL-HCC. Using machine learning modeling, we first determined an optimal cut-off point to discriminate early versus late relapses based on time to recurrence. We then constructed a risk score based on preoperative variables to forecast outcomes according to recurrence-free survival. Results: We computed an optimal cut-off point for early recurrence at 12 months post-surgery. We identified macroscopic vascular invasion, multifocal tumor, and spontaneous tumor rupture as predictor variables of outcomes associated with early recurrence and integrated them into a scoring system. We thus stratified, with high concordance, three groups of patients on a graduated scale of recurrence-related survival. Conclusion: We constructed a preoperative risk score to estimate outcomes after liver resection in NCL-HCC patients. Hence, this score makes it possible to rationally stratify patients based on recurrence risk assessment for better treatment planning.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2025-07-15T17:29:39Z
dc.date.available.none.fl_str_mv 2025-07-15T17:29:39Z
dc.date.issued.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.doi.none.fl_str_mv https: //doi.org/10.1016/j.hpb.2024.02.010
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.14703/385
dc.identifier.journal.none.fl_str_mv HPB
url https: //doi.org/10.1016/j.hpb.2024.02.010
https://hdl.handle.net/20.500.14703/385
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dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
dc.publisher.country.none.fl_str_mv UK
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv reponame:INEN-Institucional
instname:Instituto Nacional de Enfermedades Neoplásicas
instacron:INEN
instname_str Instituto Nacional de Enfermedades Neoplásicas
instacron_str INEN
institution INEN
reponame_str INEN-Institucional
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spelling PublicationRuiz, EHonles, JFernández, RUribe, KCerapio, JPCancino, KContreras-Mancilla, JCasavilca-Zambrano, SBerrospi, FPineau, PBertani, S2025-07-15T17:29:39Z2025-07-15T17:29:39Z2024https: //doi.org/10.1016/j.hpb.2024.02.010https://hdl.handle.net/20.500.14703/385HPBBackground: Liver resection is the mainstay treatment option for patients with hepatocellular carcinoma in the non-cirrhotic liver (NCL-HCC), but almost half of these patients will experience a recurrence within five years of surgery. Therefore, we aimed to develop a rationale-based risk evaluation tool to assist surgeons in recurrence-related treatment planning for NCL-HCC. Methods: We analyzed single-center data from 263 patients who underwent liver resection for NCL-HCC. Using machine learning modeling, we first determined an optimal cut-off point to discriminate early versus late relapses based on time to recurrence. We then constructed a risk score based on preoperative variables to forecast outcomes according to recurrence-free survival. Results: We computed an optimal cut-off point for early recurrence at 12 months post-surgery. We identified macroscopic vascular invasion, multifocal tumor, and spontaneous tumor rupture as predictor variables of outcomes associated with early recurrence and integrated them into a scoring system. We thus stratified, with high concordance, three groups of patients on a graduated scale of recurrence-related survival. Conclusion: We constructed a preoperative risk score to estimate outcomes after liver resection in NCL-HCC patients. Hence, this score makes it possible to rationally stratify patients based on recurrence risk assessment for better treatment planning.This work was supported by ITMO Cancer of the French National Alliance for Life Sciences and Health (Aviesan) and the French National Cancer Institute (INCa) on funds administered by the French National Institute of Health and Medical Research (Inserm), grant agreement 21CD025-00. K.C. was the recipient of a doctoral fellowship from the Research Grants for a Thesis in the South (ARTS) program of the French National Research Institute for Sustainable Development (IRD), fellowship agreement IRD-ARTS-AO2022; J.C.M. was the recipient of a doctoral fellowship from the Peruvian National Science and Technology Council (Concytec) and the World Bank on funds administered by the Peruvian National Scientific Research and Advanced Studies Program (ProCiencia), fellowship agreement 08-2018-FONDECYT/BM. The funders had no role in study design, data collection and analysis, publishing decision, or preparation of the manuscript.application/pdfengElsevier B.V.UKinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/AdultAgedCarcinoma, HepatocellularFemaleHepatectomyHumansLiver NeoplasmsMachine LearningMaleMiddle AgedNeoplasm Recurrence, LocalRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment Outcomehttps://purl.org/pe-repo/ocde/ford#3.02.21A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liverinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:INEN-Institucionalinstname:Instituto Nacional de Enfermedades Neoplásicasinstacron:INENORIGINALRuiz; 2024application/pdf1181354https://repositorio.inen.sld.pe/backend/api/core/bitstreams/8a72e4cf-a14d-4d8f-b967-a442e4008936/downloaddd3be594317ca19d46e34754ac4aff27MD51trueAnonymousREADTEXTRuiz; 2024.txtWritten by FormatFilter org.dspace.app.mediafilter.TikaTextExtractionFilter on 2025-03-29T20:33:57Z (GMT).Extracted texttext/plain58133https://repositorio.inen.sld.pe/backend/api/core/bitstreams/72b4964a-a01d-4e75-982b-ae393c9fcb11/download12f676d14f7d7c5aedf2c4546e97fd73MD54falseAnonymousREADTHUMBNAILRuiz; 2024.jpgWritten by FormatFilter org.dspace.app.mediafilter.PDFBoxThumbnail on 2025-03-29T20:33:57Z (GMT).Generated Thumbnailimage/jpeg39721https://repositorio.inen.sld.pe/backend/api/core/bitstreams/9e81dd2e-c8df-4688-92bf-2519c6490053/downloaddd53eb1db898fdc094585b7508bde7c1MD55falseAnonymousREAD20.500.14703/385oai:repositorio.inen.sld.pe:20.500.14703/3852026-02-16T20:40:04.137Zhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessopen.accesshttps://repositorio.inen.sld.peRepositorio del Instituto Nacional de Enfermedades Neoplásicasrepositorio@inen.sld.pe
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