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dc.contributor.authorMartin-Gonzalez, Paula
dc.contributor.authorde Mariscal, Estibaliz Gomez
dc.contributor.authorMartino, M Elena
dc.contributor.authorGordaliza, Pedro M
dc.contributor.authorPeligros, Isabel
dc.contributor.authorCarreras, Jose Luis
dc.contributor.authorCalvo, Felipe A
dc.contributor.authorPascau, Javier
dc.contributor.authorDesco, Manuel 
dc.contributor.authorMuñoz-Barrutia, Arrate
dc.date.accessioned2021-01-08T16:53:29Z
dc.date.available2021-01-08T16:53:29Z
dc.date.issued2020-11-30
dc.identifier.citationPLoS One. 2020; 15(11):e0242597es_ES
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/20.500.12105/11593
dc.description.abstractFew tools are available to predict tumor response to treatment. This retrospective study assesses visual and automatic heterogeneity from 18F-FDG PET images as predictors of response in locally advanced rectal cancer. This study included 37 LARC patients who underwent an 18F-FDG PET before their neoadjuvant therapy. One expert segmented the tumor from the PET images. Blinded to the patient´s outcome, two experts established by consensus a visual score for tumor heterogeneity. Metabolic and texture parameters were extracted from the tumor area. Multivariate binary logistic regression with cross-validation was used to estimate the clinical relevance of these features. Area under the ROC Curve (AUC) of each model was evaluated. Histopathological tumor regression grade was the ground-truth. Standard metabolic parameters could discriminate 50.1% of responders (AUC = 0.685). Visual heterogeneity classification showed correct assessment of the response in 75.4% of the sample (AUC = 0.759). Automatic quantitative evaluation of heterogeneity achieved a similar predictive capacity (73.1%, AUC = 0.815). A response prediction model in LARC based on tumor heterogeneity (assessed either visually or with automatic texture measurement) shows that texture features may complement the information provided by the metabolic parameters and increase prediction accuracy.es_ES
dc.description.sponsorshipThis work was partially supported by the Spanish Ministry of Economy and Competitiveness (TEC2016–78052-R, PID2019-109820RB-I00) (to AMB) and TEC2013-48251-C2 (to JP), Instituto de Salud Carlos III and European Regional Development Fund (FEDER) Funds from the European Commission, “A way of making Europe” (PI15/02121) and a Leonardo grant to Researchers and Cultural Creators 2017, BBVA Foundation (to AMB). PMG is supported by ‘Beca de Colaboracion of the Spanish Ministry of Education, Culture and Sports. The CNIC is supported by the Ministry of Economy, Industry and Competitiveness (MEIC) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).es_ES
dc.language.isoenges_ES
dc.publisherPublic Library of Science (PLOS) es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.meshAged es_ES
dc.subject.meshFeasibility Studies es_ES
dc.subject.meshFemale es_ES
dc.subject.meshFluorodeoxyglucose F18 es_ES
dc.subject.meshHumans es_ES
dc.subject.meshMale es_ES
dc.subject.meshMiddle Aged es_ES
dc.subject.meshNeoadjuvant Therapy es_ES
dc.subject.meshPositron-Emission Tomography es_ES
dc.subject.meshRadiotherapy es_ES
dc.subject.meshRectal Neoplasms es_ES
dc.subject.meshTreatment Outcome es_ES
dc.titleAssociation of visual and quantitative heterogeneity of 18F-FDG PET images with treatment response in locally advanced rectal cancer: A feasibility study.es_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID33253194es_ES
dc.format.volume15es_ES
dc.format.number11es_ES
dc.format.pagee0242597es_ES
dc.identifier.doi10.1371/journal.pone.0242597es_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España) 
dc.contributor.funderInstituto de Salud Carlos III 
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) 
dc.contributor.funderFundación BBVA 
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España) 
dc.contributor.funderMinisterio de Economía, Industria y Competitividad (España) 
dc.contributor.funderFundación ProCNIC 
dc.description.peerreviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1371/journal.pone.0242597es_ES
dc.identifier.journalPloS onees_ES
dc.repisalud.orgCNICCNIC::Unidades técnicas::Imagen Avanzadaes_ES
dc.repisalud.institucionCNICes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/TEC2016–78052-Res_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PID2019-109820RB-I00es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/TEC2013-48251-C2es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI15/02121es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/SEV-2015-0505es_ES
dc.rights.accessRightsopen accesses_ES


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