%0 Journal Article
%A Massi, Michela Carlotta
%A Gasperoni, Francesca
%A Ieva, Francesca
%A Paganoni, Anna Maria
%A Zunino, Paolo
%A Manzoni, Andrea
%A Franco, Nicola Rares
%A Veldeman, Liv
%A Ost, Piet
%A Fonteyne, Valérie
%A Talbot, Christopher J.
%A Rattay, Tim
%A Webb, Adam
%A Symonds, Paul R.
%A Johnson, Kerstie
%A Lambrecht, Maarten
%A Haustermans, Karin
%A De Meerleer, Gert
%A de Ruysscher, Dirk
%A Vanneste, Ben
%A Van Limbergen, Evert
%A Choudhury, Ananya
%A Elliott, Rebecca M.
%A Sperk, Elena
%A Herskind, Carsten
%A Veldwijk, Marlon R.
%A Avuzzi, Barbara
%A Giandini, Tommaso
%A Valdagni, Riccardo
%A Cicchetti, Alessandro
%A Azria, David
%A Jacquet, Marie-Pierre Farcy
%A Rosenstein, Barry S.
%A Stock, Richard G.
%A Collado, Kayla
%A Vega, Ana
%A Aguado-Barrera, Miguel Elías
%A Calvo, Patricia
%A Dunning, Alison M.
%A Fachal, Laura
%A Kerns, Sarah L.
%A Payne, Debbie
%A Chang-Claude, Jenny
%A Seibold, Petra
%A West, Catharine M. L.
%A Rancati, Tiziana
%T A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort
%J Frontiers in oncology
%V 10
%@ 2234-943X
%C Lausanne
%I Frontiers Media
%M DKFZ-2020-02361
%P 541281
%D 2020
%F PUB:(DE-HGF)16
%9 Journal Article
%R 10.3389/fonc.2020.541281
%U https://inrepo02.dkfz.de/record/164278