Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy

Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy

In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools...

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Bibliographic Details
Main Authors: Lars J. Isaksson, Matteo Pepa, Mattia Zaffaroni, Giulia Marvaso, Daniela Alterio, Stefania Volpe, Giulia Corrao, Matteo Augugliaro, Anna Starzyńska, Maria C. Leonardi, Roberto Orecchia, Barbara A. Jereczek-Fossa
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-06-01
Series:Frontiers in Oncology
Subjects:
radiotherapy
toxicity
predictive models
machine-learning
radiomics
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2020.00790/full

Internet

https://www.frontiersin.org/article/10.3389/fonc.2020.00790/full

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