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

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

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using...

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Bibliographic Details
Main Authors: Michela Carlotta Massi, Francesca Gasperoni, Francesca Ieva, Anna Maria Paganoni, Paolo Zunino, Andrea Manzoni, Nicola Rares Franco, Liv Veldeman, Piet Ost, Valérie Fonteyne, Christopher J. Talbot, Tim Rattay, Adam Webb, Paul R. Symonds, Kerstie Johnson, Maarten Lambrecht, Karin Haustermans, Gert De Meerleer, Dirk de Ruysscher, Ben Vanneste, Evert 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 Elías 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
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-10-01
Series:Frontiers in Oncology
Subjects:
prostate cancer
late toxicity
snps
deep learning
autoencoder
validation
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2020.541281/full

Internet

https://www.frontiersin.org/article/10.3389/fonc.2020.541281/full

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