New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models

[EN] Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancerous processes in early stages. As for breast cancer, due to the tubular structure of the tissue, being formed by ducts, anisotropic diffusion should be considered instead of the general isotropic d...

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Autores: Ortiz-Abellan, C., Aguado-Sarrió, E., Camps-Herrero, J., Prats-Montalbán, José Manuel|||0000-0001-6294-4486, Ferrer, Alberto|||0000-0001-7244-5947
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/220057
Acceso en línea:https://riunet.upv.es/handle/10251/220057
Access Level:acceso abierto
Palabra clave:Magnetic resonance imaging
Cancerous processes
Breast cancer
Diffusion Tensor Imaging (DTI)
Multivariate Curve Resolution (MCR) models
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spelling New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) modelsOrtiz-Abellan, C.Aguado-Sarrió, E.Camps-Herrero, J.Prats-Montalbán, José Manuel|||0000-0001-6294-4486Ferrer, Alberto|||0000-0001-7244-5947Magnetic resonance imagingCancerous processesBreast cancerDiffusion Tensor Imaging (DTI)Multivariate Curve Resolution (MCR) models[EN] Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancerous processes in early stages. As for breast cancer, due to the tubular structure of the tissue, being formed by ducts, anisotropic diffusion should be considered instead of the general isotropic diffusion. Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied by changing the magnetic field in several spatial directions. To date, the application of Multivariate Curve Resolution (MCR) models in diffusion sequences has demonstrated its ability to develop cancer biomarkers of easy clinical interpretation in the case of isotropic tissues, such as the prostate. But so far, it has never been applied in the case of anisotropic tissues, as the breast. Therefore, the main objective of this work is to obtain easy-to-interpret imaging biomarkers useful for early breast cancer diagnosis from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models. A classification model to identify healthy and tumor affected pixels is also proposed.This research was supported by the Spanish Government (Science and Innovation Ministry) under the project PID2020-119262RB-I00, and by the Generalitat Valenciana under the project AICO/2021/111.ElsevierGrupo de Ingeniería Estadística Multivariante GIEMDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadEscuela Técnica Superior de Ingeniería IndustrialGENERALITAT VALENCIANAAGENCIA ESTATAL DE INVESTIGACIONUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-08-15journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/220057reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-119262RB-I00 TECNICAS ESTADISTICAS MULTIVARIANTES BASADAS EN VARIABLES LATENTES PARA EL DESARROLLO DE BIOMARCADORES DE IMAGEN PARA LA DIAGNOSIS Y PROGNOSIS DE CANCER DE MAMAGeneralitat Valenciana https://doi.org/10.13039/501100003359 AICO%2F2021%2F111 OPTIMIZACIÓN DE PROCESOS EN LA INDUSTRIA 4.0 MEDIANTE TÉCNICAS ESTADÍSTICAS MULTIVARIANTES (INDOPT4.0)open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2200572026-06-13T07:49:27Z
dc.title.none.fl_str_mv New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models
title New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models
spellingShingle New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models
Ortiz-Abellan, C.
Magnetic resonance imaging
Cancerous processes
Breast cancer
Diffusion Tensor Imaging (DTI)
Multivariate Curve Resolution (MCR) models
title_short New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models
title_full New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models
title_fullStr New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models
title_full_unstemmed New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models
title_sort New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models
dc.creator.none.fl_str_mv Ortiz-Abellan, C.
Aguado-Sarrió, E.
Camps-Herrero, J.
Prats-Montalbán, José Manuel|||0000-0001-6294-4486
Ferrer, Alberto|||0000-0001-7244-5947
author Ortiz-Abellan, C.
author_facet Ortiz-Abellan, C.
Aguado-Sarrió, E.
Camps-Herrero, J.
Prats-Montalbán, José Manuel|||0000-0001-6294-4486
Ferrer, Alberto|||0000-0001-7244-5947
author_role author
author2 Aguado-Sarrió, E.
Camps-Herrero, J.
Prats-Montalbán, José Manuel|||0000-0001-6294-4486
Ferrer, Alberto|||0000-0001-7244-5947
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Grupo de Ingeniería Estadística Multivariante GIEM
Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Escuela Técnica Superior de Ingeniería Industrial
GENERALITAT VALENCIANA
AGENCIA ESTATAL DE INVESTIGACION
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Magnetic resonance imaging
Cancerous processes
Breast cancer
Diffusion Tensor Imaging (DTI)
Multivariate Curve Resolution (MCR) models
topic Magnetic resonance imaging
Cancerous processes
Breast cancer
Diffusion Tensor Imaging (DTI)
Multivariate Curve Resolution (MCR) models
description [EN] Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancerous processes in early stages. As for breast cancer, due to the tubular structure of the tissue, being formed by ducts, anisotropic diffusion should be considered instead of the general isotropic diffusion. Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied by changing the magnetic field in several spatial directions. To date, the application of Multivariate Curve Resolution (MCR) models in diffusion sequences has demonstrated its ability to develop cancer biomarkers of easy clinical interpretation in the case of isotropic tissues, such as the prostate. But so far, it has never been applied in the case of anisotropic tissues, as the breast. Therefore, the main objective of this work is to obtain easy-to-interpret imaging biomarkers useful for early breast cancer diagnosis from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models. A classification model to identify healthy and tumor affected pixels is also proposed.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-08-15
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/220057
url https://riunet.upv.es/handle/10251/220057
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-119262RB-I00 TECNICAS ESTADISTICAS MULTIVARIANTES BASADAS EN VARIABLES LATENTES PARA EL DESARROLLO DE BIOMARCADORES DE IMAGEN PARA LA DIAGNOSIS Y PROGNOSIS DE CANCER DE MAMA
Generalitat Valenciana https://doi.org/10.13039/501100003359 AICO%2F2021%2F111 OPTIMIZACIÓN DE PROCESOS EN LA INDUSTRIA 4.0 MEDIANTE TÉCNICAS ESTADÍSTICAS MULTIVARIANTES (INDOPT4.0)
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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