Telomere Length Dynamics and Chromosomal Instability for Predicting Individual Radiosensitivity and Risk via Machine Learning

Telomere Length Dynamics and Chromosomal Instability for Predicting Individual Radiosensitivity and Risk via Machine Learning

The ability to predict a cancer patient’s response to radiotherapy and risk of developing adverse late health effects would greatly improve personalized treatment regimens and individual outcomes. Telomeres represent a compelling biomarker of individual radiosensitivity and risk, as exposure can res...

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Bibliographic Details
Main Authors: Jared J. Luxton, Miles J. McKenna, Aidan M. Lewis, Lynn E. Taylor, Sameer G. Jhavar, Gregory P. Swanson, Susan M. Bailey
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Journal of Personalized Medicine
Subjects:
telomeres
chromosomal instability
inversions
prostate cancer
IMRT
machine learning
Online Access:https://www.mdpi.com/2075-4426/11/3/188

Internet

https://www.mdpi.com/2075-4426/11/3/188

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