Dosimetric features‐driven machine learning model for DVH prediction in VMAT treatment planning
Author:
Affiliation:
1. Department of Radiation Oncology Stanford University 875 Blake Wilbur Drive Stanford CA 94305‐5847 USA
Funder
National Institutes of Health
Varian Medical Systems
Publisher
Wiley
Subject
General Medicine
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13334
Reference21 articles.
1. Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans
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4. A method for a priori estimation of best feasible DVH for organs-at-risk: Validation for head and neck VMAT planning
5. Volumetric modulated arc therapy: a review of current literature and clinical use in practice
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