A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort

Michela Carlotta Massi, Francesca Gasperoni, Francesca Ieva*, Anna Maria Paganoni, Paolo Zunino, Andrea Manzoni, Nicola Rares Franco, Liv Veldeman, Piet Ost, Valerie Fonteyne, Christopher J. Talbot, Tim Rattay, Adam Webb, Paul R. Symonds, Kerstie Johnson, Maarten Lambrecht, Karin Haustermans, Gert De Meerleer, Dirk de Ruysscher, Ben VannesteEvert Van Limbergen, Ananya Choudhury, Rebecca M. Elliott, Elena Sperk, Carsten Herskind, Marlon R. Veldwijk, Barbara Avuzzi, Tommaso Giandini, Riccardo Valdagni, Alessandro Cicchetti, David Azria, Marie-Pierre Farcy Jacquet, Barry S. Rosenstein, Richard G. Stock, Kayla Collado, Ana Vega, Miguel Elias Aguado-Barrera, Patricia Calvo, Alison M. Dunning, Laura Fachal, Sarah L. Kerns, Debbie Payne, Jenny Chang-Claude, Petra Seibold, Catharine M. L. West, Tiziana Rancati, REQUITE consortium

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Web of Science)
Original languageEnglish
Article number541281
Number of pages15
JournalFrontiers in Oncology
Volume10
DOIs
Publication statusPublished - 15 Oct 2020

Keywords

  • prostate cancer
  • late toxicity
  • snps
  • deep learning
  • autoencoder
  • validation
  • GENOME-WIDE ASSOCIATION
  • QUALITY-OF-LIFE
  • RADIATION-THERAPY
  • RADIOGENOMICS
  • METAANALYSIS
  • CONSORTIUM
  • BIOMARKERS
  • SELECTION
  • VARIANTS
  • DESIGN

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