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On the prediction of Hodgkin lymphoma treatment response

dc.contributor.authorAndrés Galiana, Enrique Juan de 
dc.contributor.authorFernández Martínez, Juan Luis 
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.contributor.authorCoz Velasco, Juan José del 
dc.contributor.authorFernández, R.
dc.contributor.authorSolano, Julia
dc.contributor.authorNogués, E. A.
dc.contributor.authorGonzález Rodríguez, Ana Pilar 
dc.date.accessioned2015-10-02T07:14:53Z
dc.date.available2015-10-02T07:14:53Z
dc.date.issued2015
dc.identifier.citationClinical and Translational Oncology, 17(8), p. (2015); doi:10.1007/s12094-015-1285-z
dc.identifier.issn1699-048X
dc.identifier.urihttp://hdl.handle.net/10651/33362
dc.description.abstractThe cure rate in Hodgkin lymphoma is high, but the response along with treatment is still unpredictable and highly variable among patients. Detecting those patients who do not respond to treatment at early stages could bring improvements in their treatment. This research tries to identify the main biological prognostic variables currently gathered at diagnosis and design a simple machine learning methodology to help physicians improve the treatment response assessment
dc.description.sponsorshipEnrique J. de Andrés was supported by the Spanish Ministerio de Economía y Competitividad (Grant TIN2011-23558), and the medical analysis was supported by the Fondo de Investigaciones Sanitarias (Instituto Carlos III-Grant PI12/01280). No other financial support has been received to perform this retrospective analysis
dc.description.statementofresponsibilityde Andrés-Galiana, E.J., Fernández-Martínez, J.L., Luaces, O., del Coz, J.J., Fernández, R., Solano, J., Nogués, E.A., Zanabilli, Y., Alonso, J.M., Payer, A.R., Vicente, J.M., Medina, J., Taboada, F., Vargas, M., Alarcón, C., Morán, M., González-Ordóñez, A., Palicio, M.A., Ortiz, S., Chamorro, C., Gonzalez, S., González-Rodríguez, A.P.
dc.format.extentp. 612-619
dc.language.isoeng
dc.relation.ispartofClinical and Translational Oncology, 17(8)
dc.rights© 2015 Springer
dc.subjectHodgkin lymphoma
dc.subjectTreatment response
dc.subjectMachine learning
dc.titleOn the prediction of Hodgkin lymphoma treatment response
dc.typejournal article
dc.identifier.doi10.1007/s12094-015-1285-z
dc.relation.projectIDMEC/TIN2011-23558
dc.relation.projectIDFondo de Investigaciones Sanitarias-Instituto Carlos III/PI12-01280
dc.relation.publisherversionhttp://dx.doi.org/10.1007/s12094-015-1285-z
dc.rights.accessRightsopen access
dc.type.hasVersionAM


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