Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy

Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy

Severe acute dysphagia commonly results from head and neck radiotherapy (RT). A model enabling prediction of severity of acute dysphagia for individual patients could guide clinical decision-making. Statistical associations between RT dose distributions and dysphagia could inform RT planning protoco...

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Bibliographic Details
Main Authors: Jamie Dean, Kee Wong, Hiram Gay, Liam Welsh, Ann-Britt Jones, Ulricke Schick, Jung Hun Oh, Aditya Apte, Kate Newbold, Shreerang Bhide, Kevin Harrington, Joseph Deasy, Christopher Nutting, Sarah Gulliford
Format: Article
Language:English
Published: Elsevier 2018-01-01
Series:Clinical and Translational Radiation Oncology
Online Access:http://www.sciencedirect.com/science/article/pii/S2405630817300460

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http://www.sciencedirect.com/science/article/pii/S2405630817300460

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