Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort

Purpose: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) alg...

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Main Authors: Mahmoud Aldraimli, PhD, Sarah Osman, PhD, Diana Grishchuck, MSc, Samuel Ingram, MSc, Robert Lyon, PhD, Anil Mistry, MSc, Jorge Oliveira, PhD, Robert Samuel, MBChB, Leila E.A. Shelley, PhD, Daniele Soria, PhD, Miriam V. Dwek, PhD, Miguel E. Aguado-Barrera, MD, PhD, David Azria, MD, Jenny Chang-Claude, PhD, Alison Dunning, PhD, Alexandra Giraldo, MD, Sheryl Green, MD, Sara Gutiérrez-Enríquez, PhD, Carsten Herskind, PhD, Hans van Hulle, MD, Maarten Lambrecht, MD, Laura Lozza, MD, Tiziana Rancati, MSc, Victoria Reyes, MD, Barry S. Rosenstein, PhD, Dirk de Ruysscher, MD, Maria C. de Santis, MD, Petra Seibold, PhD, Elena Sperk, MD, R. Paul Symonds, MD, Hilary Stobart, Begoña Taboada-Valadares, MD, Christopher J. Talbot, PhD, Vincent J.L. Vakaet, MD, Ana Vega, PhD, Liv Veldeman, MD, PhD, Marlon R. Veldwijk, PhD, Adam Webb, PhD, Caroline Weltens, MD, Catharine M. West, PhD, Thierry J. Chaussalet, PhD, Tim Rattay, MBChB, PhD
Format: Article
Language:English
Published: Elsevier 2022-05-01
Series:Advances in Radiation Oncology
Online Access:http://www.sciencedirect.com/science/article/pii/S2452109421002487

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