Journal
Clinical and translational radiation oncology
Publication Date
2018
Volume
8
Inclusive Pages
27-39
Document Type
Open Access Publication
DOI
10.1016/j.ctro.2017.11.009
Rights and Permissions
Dean J, Wong K, Gay H, et al (2018) Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy. Clinical and Translational Radiation Oncology, 8:27-39. DOI:https://doi.org/10.1016/j.ctro.2017.11.009 Copyright 2017 The Authors. Published by Elsevier Ireland Ltd on behalf of European Society for Radiotherapy and Oncology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Recommended Citation
Dean, Jamie; Wong, Kee; Gay, Hiram; Welsh, Liam; Jones, Ann-Britt; Schick, Ulricke; Oh, Jung Hun; Apte, Aditya; Newbold, Kate; Bhide, Shreerang; Harrington, Kevin; Deasy, Joseph; Nutting, Christopher; and Gulliford, Sarah, "Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy." Clinical and translational radiation oncology. 8, 27-39. (2018).
https://digitalcommons.wustl.edu/open_access_pubs/6702
