Torna al Registre Simple

 

Enhancing Tumor Microstructural Quantification With Machine Learning and Diffusion-Relaxation MRI

dc.contributorVall d'Hebron Barcelona Hospital Campus
dc.contributor.authorMacarro, Carlos
dc.contributor.authorBernatowicz, Kinga
dc.contributor.authorGarcía Ruiz, Alonso
dc.contributor.authorMonreal-Agüero, Camilo
dc.contributor.authorCorral Gámez, Juanfra
dc.contributor.authorMerino Casabiel, Xavier
dc.contributor.authorMast, Richard
dc.contributor.authorSerna, Garazi
dc.contributor.authorSimonetti, Sara
dc.contributor.authorRoson Gradaille, Nuria
dc.contributor.authorVieito , Maria
dc.contributor.authorESCOBAR, MANUEL
dc.contributor.authorToledo, Rodrigo
dc.contributor.authorNuciforo, Paolo
dc.contributor.authorGARRALDA, Elena
dc.contributor.authorPerez-Lopez, Raquel
dc.contributor.authorGrussu, Francesco
dc.date.accessioned2025-03-03T11:09:07Z
dc.date.available2025-03-03T11:09:07Z
dc.date.copyright2024
dc.date.issued2025-02
dc.identifier.citationMacarro C, Bernatowicz K, Garcia-Ruiz A, Serna G, Monreal-Agüero C, Simonetti S, et al. Enhancing Tumor Microstructural Quantification With Machine Learning and Diffusion-Relaxation MRI. J Magn Reson Imaging. 2025 Feb;61(2):1018–21.
dc.identifier.issn1522-2586
dc.identifier.urihttps://hdl.handle.net/11351/12681
dc.descriptionQuantificació microestructural del tumor; Aprenentatge automàtic; Ressonància magnètica de difusió-relaxació
dc.description.sponsorshipWe thank the whole medical oncology, radiology, pathology, molecular biology, clinical trial, and IT teams at the Vall d'Hebron Campus. We would also like to express our sincere gratitude to all patients and their families for dedicating their time to research. VHIO acknowledges the State Agency for Research (Agencia Estatal de Investigación) for the financial support as a Center of Excellence Severo Ochoa (CEX2020-001024-S/AEI/10.13039/501100011033), the Cellex Foundation for providing research facilities and equipment and the CERCA Programme from the Generalitat de Catalunya for their support. This research has been supported by PREdICT, sponsored by AstraZeneca. This study has been co-funded by the European Regional Development Fund/European Social Fund “A way to make Europe” (to R.P.L.). RPL is supported by “la Caixa” Foundation, the Prostate Cancer Foundation (18YOUN19), a CRIS Foundation Talent Award (TALENT19-05), the FERO Foundation through the XVIII Fero Fellowship for Oncological Research, the Instituto de Salud Carlos III-Investigación en Salud (PI18/01395 and PI21/01019), the Asociación Española Contra el Cancer (AECC) (PRYCO211023SERR) and the Agency for Management of University and Research Grants of Catalonia (AGAUR) (2023PROD00178). This research has been funded by the CaixaResearch Advanced Oncology Research Program supported by “La Caixa” Foundation (to R.P.L.). The project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/PR22/11920010”, funding F.G. This research has received support from the Beatriu de Pinós Postdoctoral Program from the Secretariat of Universities and Research of the Department of Business and Knowledge of the Government of Catalonia, and the support from the Marie Sklodowska-Curie COFUND program (BP3, contract number 801370; reference 2019 BP 00182) of the H2020 program (to K.B.). C.M. is supported by the Asociación Española Contra el Cancer (PRYCO211023SERR). VHIO is also grateful to the Generalitat de Catalunya, Comissió Interdepartamental de Recerca i Innovació Tecnològica.
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofseriesJournal of Magnetic Resonance Imaging;61(2)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScientia
dc.subjectCàncer
dc.subjectAprenentatge automàtic
dc.subjectImatgeria per ressonància magnètica
dc.subject.meshMachine Learning
dc.subject.meshDiffusion Magnetic Resonance Imaging
dc.subject.meshNeoplasms
dc.titleEnhancing Tumor Microstructural Quantification With Machine Learning and Diffusion-Relaxation MRI
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1002/jmri.29484
dc.subject.decsaprendizaje automático
dc.subject.decsimagen de resonancia magnética de difusión
dc.subject.decsneoplasias
dc.relation.publishversionhttps://doi.org/10.1002/jmri.29484
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.audienceProfessionals
dc.contributor.organismesInstitut Català de la Salut
dc.contributor.authoraffiliation[Macarro C, Bernatowicz K, Garcia-Ruiz A, Serna G, Monreal-Agüero C, Simonetti S, Toledo R, Nuciforo P, Perez-Lopez R, Grussu F] Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Corral JF, Merino X, Mast R, Roson N, Escobar M] Servei de Radiodiagnòstic, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Institut de Diagnòstic per la Imatge (IDI), Barcelona, Spain. [Vieito M, Garralda E] Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Servei d’Oncologia Mèdica, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain
dc.identifier.pmid38895928
dc.identifier.wos001250612900001
dc.relation.projectidinfo:eu-repo/grantAgreement/ES/PE2013-2016/PI18%2F01395
dc.relation.projectidinfo:eu-repo/grantAgreement/ES/PE2017-2020/PI21%2F01019
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess


Fitxers en aquest element

Portada del document
Name:
enhancing-tumor-microstructura ...
Grandària:
4.898Mb
Format:
PDF
Description:
Enhancing Tumor Microstructural ...
Visualitza

Aquest element apareix en la col·lecció o col·leccions següent(s)

Torna al Registre Simple