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

dc.contributor.author	Brendlin, Andreas Stefan
dc.contributor.author	Peisen, Felix Ludwig
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
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	Article	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

Files in this item

Files	Size	Format	View
There are no files associated with this item.