A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma

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dc.contributor.author Brendlin, Andreas Stefan
dc.contributor.author Peisen, Felix
dc.contributor.author Afat, Saif
dc.contributor.author Amaral, Teresa
dc.contributor.author Nikolaou, Konstantin
dc.contributor.author Al Mansour, Haidara
dc.contributor.author Eigentler, Thomas Kurt
dc.contributor.author Othman, Ahmed
dc.date.accessioned 2022-07-05T10:14:24Z
dc.date.available 2022-07-05T10:14:24Z
dc.date.issued 2021
dc.identifier.issn 2051-1426
dc.identifier.uri http://hdl.handle.net/10900/129052
dc.language.iso en de_DE
dc.publisher London de_DE
dc.relation.uri http://dx.doi.org/10.1136/jitc-2021-003261 de_DE
dc.subject.ddc 570 de_DE
dc.subject.ddc 610 de_DE
dc.title A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma de_DE
dc.type Artikel de_DE
utue.quellen.id 20220404000000_00670
utue.personen.roh Brendlin, Andreas Stefan
utue.personen.roh Peisen, Felix
utue.personen.roh Almansour, Haidara
utue.personen.roh Afat, Saif
utue.personen.roh Eigentler, Thomas
utue.personen.roh Amaral, Teresa
utue.personen.roh Faby, Sebastian
utue.personen.roh Calvarons, Adria Font
utue.personen.roh Nikolaou, Konstantin
utue.personen.roh Othman, Ahmed E.
dcterms.isPartOf.ZSTitelID Journal For Immunotherapy of Cancer de_DE
dcterms.isPartOf.ZS-Issue Article e003261 de_DE
dcterms.isPartOf.ZS-Volume 9 (11) de_DE
utue.fakultaet 04 Medizinische Fakultät de_DE


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