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Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

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Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

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dc.contributor.author Pérez-Benito, Francisco Javier es_ES
dc.contributor.author Signol, François es_ES
dc.contributor.author Perez-Cortes, Juan-Carlos es_ES
dc.contributor.author Pollán, Marina es_ES
dc.contributor.author Perez-Gómez, Beatriz es_ES
dc.contributor.author Salas-Trejo, Dolores es_ES
dc.contributor.author Casals, María es_ES
dc.contributor.author Martinez, Inmaculada es_ES
dc.contributor.author Llobet Azpitarte, Rafael es_ES
dc.date.accessioned 2020-10-06T03:32:14Z
dc.date.available 2020-10-06T03:32:14Z
dc.date.issued 2019-08 es_ES
dc.identifier.issn 0169-2607 es_ES
dc.identifier.uri http://hdl.handle.net/10251/151168
dc.description.abstract [EN] Background The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. Methods A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. Results The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. Conclusion Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study. es_ES
dc.description.sponsorship This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2018/1, and by Carlos III Institute of Health under the project DTS15/00080 es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation IVACE/IMAMCN/2018/1 (ID-II3D) es_ES
dc.relation INSTITUTO DE SALUD CARLOS III/DTS15/00080 es_ES
dc.relation.ispartof Computer Methods and Programs in Biomedicine es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Breast density es_ES
dc.subject Texture features es_ES
dc.subject Cancer development risk es_ES
dc.subject Breast cancer es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cmpb.2019.05.022 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. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Pérez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Pollán, M.; Perez-Gómez, B.; Salas-Trejo, D.; Casals, M.... (2019). Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Computer Methods and Programs in Biomedicine. 177:123-132. https://doi.org/10.1016/j.cmpb.2019.05.022 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.cmpb.2019.05.022 es_ES
dc.description.upvformatpinicio 123 es_ES
dc.description.upvformatpfin 132 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 177 es_ES
dc.identifier.pmid 31319940 es_ES
dc.relation.pasarela S\403944 es_ES
dc.contributor.funder Institut Valencià de Competitivitat Empresarial es_ES
dc.contributor.funder Instituto de Salud Carlos III es_ES


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