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Starke, S.* ; Leger, S.* ; Zwanenburg, A.* ; Pilz, K.* ; Lohaus, F.* ; Linge, A.* ; Zoephel, K.* ; Kotzerke, J.* ; Schreiber, A.* ; Tinhofer, I.* ; Budach, V.* ; Stuschke, M.* ; Balermpas, P.* ; Roedel, C.* ; Ganswindt, U. ; Belka, C. ; Pigorsch, S.* ; Combs, S.E. ; Moennich, D.* ; Zips, D.* ; Krause, M.* ; Baumann, M.* ; Richter, C.* ; Troost, E.G.C.* ; Loeck, S.*

Deep-learning based estimation of loco-regional control for patients with locally advanced HNSCC.

Radiother. Oncol. 133, S254-S255 (2019)
Open Access Green as soon as Postprint is submitted to ZB.
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Publication type Article: Journal article
Document type Meeting abstract
Language english
Publication Year 2019
HGF-reported in Year 2019
ISSN (print) / ISBN 0167-8140
e-ISSN 1879-0887
Journal Radiotherapy and Oncology
Quellenangaben Volume: 133, Issue: , Pages: S254-S255 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland
Reviewing status Peer reviewed
Institute(s) Translational Metabolic Oncology (IDC-TMO)
Institute of Radiation Medicine (IRM)
POF-Topic(s) 30504 - Mechanisms of Genetic and Environmental Influences on Health and Disease
30203 - Molecular Targets and Therapies
Research field(s) Radiation Sciences
PSP Element(s) G-521800-001
G-501300-001
WOS ID WOS:000468315601122
Erfassungsdatum 2019-06-14
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