A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multinational cohort

Keywords

Statistical learning
Health Analytics
Living Systems and Precision Medicine
Code:
30/2020
Title:
A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multinational cohort
Date:
Saturday 9th May 2020
Author(s):
Massi, M.C., Gasperoni, F., Ieva, F., Paganoni, A.M., Zunino, P., Manzoni, A., Franco, N.R., et al.
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Abstract:
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 a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors.
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Pubblicato su "Frontiers in Oncology" DOI: https://doi.org/10.3389/fonc.2020.541281