Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

PurposeThe purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands.Material and m...

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Bibliographic Details
Main Authors: Hubert S. Gabryś, Florian Buettner, Florian Sterzing, Henrik Hauswald, Mark Bangert
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-03-01
Series:Frontiers in Oncology
Subjects:
radiotherapy
IMRT
NTCP
xerostomia
head and neck
machine learning
Online Access:http://journal.frontiersin.org/article/10.3389/fonc.2018.00035/full

Internet

http://journal.frontiersin.org/article/10.3389/fonc.2018.00035/full

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