A preoperative risk score based on early recurrence for estimating outcomes after resection of hepatocellular carcinoma in the non-cirrhotic liver
Descripción del Articulo
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...
| Autores: | , , , , , , , , , , |
|---|---|
| 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 |
| id |
INEN_1fb22df2276d8d8082ff4bdff053da79 |
|---|---|
| oai_identifier_str |
oai:repositorio.inen.sld.pe:20.500.14703/385 |
| network_acronym_str |
INEN |
| network_name_str |
INEN-Institucional |
| repository_id_str |
. |
| 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 |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| 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 |
| identifier_str_mv |
HPB |
| 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 |
| collection |
INEN-Institucional |
| bitstream.url.fl_str_mv |
https://repositorio.inen.sld.pe/backend/api/core/bitstreams/8a72e4cf-a14d-4d8f-b967-a442e4008936/download https://repositorio.inen.sld.pe/backend/api/core/bitstreams/72b4964a-a01d-4e75-982b-ae393c9fcb11/download https://repositorio.inen.sld.pe/backend/api/core/bitstreams/9e81dd2e-c8df-4688-92bf-2519c6490053/download |
| bitstream.checksum.fl_str_mv |
dd3be594317ca19d46e34754ac4aff27 12f676d14f7d7c5aedf2c4546e97fd73 dd53eb1db898fdc094585b7508bde7c1 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio del Instituto Nacional de Enfermedades Neoplásicas |
| repository.mail.fl_str_mv |
repositorio@inen.sld.pe |
| _version_ |
1864633491103154176 |
| 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 |
| score |
13.486191 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).