1. Ktisis Cyprus University of Technology
  2. Cyprus University of Technology (Research Output)
  3. Άρθρα/Articles
Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33232
DC FieldValueLanguage
dc.contributor.authorHesso, Iman-
dc.contributor.authorKayyali, Reem-
dc.contributor.authorZacharias, Lithin-
dc.contributor.authorCharalambous, Andreas-
dc.contributor.authorLavdaniti, Maria-
dc.contributor.authorStalika, Evangelia-
dc.contributor.authorAjami, Tarek-
dc.contributor.authorAcampa, Wanda-
dc.contributor.authorBoban, Jasmina-
dc.contributor.authorGebara, Shereen Nabhani-
dc.date.accessioned2024-11-29T10:47:22Z-
dc.date.available2024-11-29T10:47:22Z-
dc.date.issued2024-03-
dc.identifier.citationJournal of cancer policy, 2024, vol. 39en_US
dc.identifier.issn22135383-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/33232-
dc.description.abstractCancer poses significant challenges for healthcare professionals across the disease pathway including cancer imaging. This study constitutes part of the user requirement definition of INCISIVE EU project. The project has been designed to explore the full potential of artificial intelligence (AI)-based technologies in cancer imaging to streamline diagnosis and management. The study aimed to map cancer care pathways (breast, prostate, colorectal and lung cancers) across INCISIVE partner countries, and identify bottle necks within these pathways.en_US
dc.description.sponsorshipHorizon 2020 Framework Programme Horizon 2020 European Commissionen_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Cancer Policyen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectTreatmenten_US
dc.subjectCanceren_US
dc.subjectCancer careen_US
dc.subjectCare pathwayen_US
dc.subjectComparative analysisen_US
dc.subjectDelayen_US
dc.subjectDiagnosisen_US
dc.titleCancer care pathways across seven countries in Europe: What are the current obstacles? And how can artificial intelligence help?en_US
dc.typeArticleen_US
dc.collaborationKingston University Londonen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationInternational Hellenic Universityen_US
dc.collaborationAristotle University of Thessalonikien_US
dc.subject.categoryMedical Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.countryGreeceen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.jcpo.2023.100457en_US
dc.identifier.pmid38008356-
dc.identifier.scopus2-s2.0-85178994375-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85178994375-
dc.relation.volume39en_US
cut.common.academicyear2024-2025en_US
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.author.deptDepartment of Nursing-
crisitem.author.facultyFaculty of Health Sciences-
crisitem.author.orcid0000-0003-4050-031X-
crisitem.author.parentorgFaculty of Health Sciences-
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