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Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

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Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

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dc.contributor.author Llobet Azpitarte, Rafael es_ES
dc.contributor.author Pollán, Marina es_ES
dc.contributor.author Antón Guirao, Joaquín es_ES
dc.contributor.author Miranda-García, Josefa es_ES
dc.contributor.author Casals el Busto, María es_ES
dc.contributor.author Martinez Gomez, Inmaculada es_ES
dc.contributor.author Ruiz Perales, Francisco es_ES
dc.contributor.author Pérez Gómez, Beatriz es_ES
dc.contributor.author Salas-Trejo, Dolores es_ES
dc.contributor.author Perez-Cortes, Juan-Carlos es_ES
dc.date.accessioned 2015-07-06T06:43:51Z
dc.date.available 2015-07-06T06:43:51Z
dc.date.issued 2014-09
dc.identifier.issn 0169-2607
dc.identifier.issn 1872-7565
dc.identifier.uri http://hdl.handle.net/10251/52701
dc.description.abstract The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density(MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available. (C) 2014 Elsevier Ireland Ltd. All rights reserved. es_ES
dc.description.sponsorship This work was supported by research grants from Gent per Gent Fund (EDEMAC Project); Spain's Health Research Fund (Fondo de Investigacion Santiaria) (PI060386 & FIS PS09/00790); Spanish MICINN grants TIN2009-14205-C04-02 and Consolider-Ingenio 2010: MIPRCV (CSD2007-00018); Spanish Federation of Breast Cancer Patients (Federacion Espanola de Cancer de Mama) (FECMA 485 EPY 1170-10). The English revision of this paper was funded by the Universitat Politecnica de Valencia, Spain. en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computer Methods and Programs in Biomedicine es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Mammographic density es_ES
dc.subject Automated density assessment es_ES
dc.subject Computer-aided diagnosis es_ES
dc.subject Computer image analysis es_ES
dc.subject Breast cancer risk es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cmpb.2014.01.021
dc.relation.projectID info:eu-repo/grantAgreement/ISCIII//PI06%2F0386/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ISCIII//PS09%2F00790/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2009-14205-C04-02/ES/Tecnicas Interactivas Y Adaptativas Para Sistemas Automaticos De Reconocimiento, Aprendizaje Y Percepcion/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MEC//CSD2007-00018/ES/Multimodal Intraction in Pattern Recognition and Computer Visionm/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/FECMA//485 EPY 1170–10/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Área de Instituto de Ciencias de la Educación - Àrea de l'Institut de Ciències de l'Educació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Llobet Azpitarte, R.; Pollán, M.; Antón Guirao, J.; Miranda-García, J.; Casals El Busto, M.; Martinez Gomez, I.; Ruiz Perales, F.... (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine. 116(2):105-115. https://doi.org/10.1016/j.cmpb.2014.01.021 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.cmpb.2014.01.021 es_ES
dc.description.upvformatpinicio 105 es_ES
dc.description.upvformatpfin 115 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 116 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 280635
dc.contributor.funder Ministerio de Educación y Ciencia es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Instituto de Salud Carlos III; Fondo de Investigaciones Sanitarias es_ES
dc.contributor.funder Federación Española de Cáncer de Mama es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Fundación Gent per Gent es_ES


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