Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation

Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation

Background and purpose: A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. Despite the LKB model’s popularity, it can suffer from numerical instability and considers only the generalized mea...

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Bibliographic Details
Main Authors: Pratik Samant, Dirk de Ruysscher, Frank Hoebers, Richard Canters, Emma Hall, Chris Nutting, Tim Maughan, Frank Van den Heuvel
Format: Article
Language:English
Published: Elsevier 2023-03-01
Series:Clinical and Translational Radiation Oncology
Subjects:
Normal Tissue Complication Probability
Treatment Planning
Radiotherapy
Machine Learning
Xerostomia
Head and Neck Cancer
Online Access:http://www.sciencedirect.com/science/article/pii/S2405630823000204

Internet

http://www.sciencedirect.com/science/article/pii/S2405630823000204

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