Re-evaluation of the prospective risk analysis for artificial-intelligence driven cone beam computed tomography-based online adaptive radiotherapy after one year of clinical experience

Author:

Wegener SonjaORCID,Käthner Paul,Weick Stefan,Schindhelm Robert,Breuer Kathrin,Stark Silke,Hutzel Heike,Lutyj Paul,Zimmermann Marcus,Tamihardja Jörg,Wittig Andrea,Exner Florian,Razinskas Gary

Funder

Interdisziplinäres Zentrum für Klinische Forschung, Universitätsklinikum Würzburg

Julius-Maximilians-Universität Würzburg

Publisher

Elsevier BV

Reference20 articles.

1. Making on-line adaptive radiotherapy possible using artificial intelligence and machine learning for efficient daily re-planning;Archambault;Med Phys Int J,2020

2. Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region;Sibolt;Phys Imaging Radiat Oncol,2020

3. Online adaptive radiotherapy for bladder cancer using a simultaneous integrated boost and fiducial markers;Azzarouali;Radiat Oncol,2023

4. Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time;Byrne;J Appl Clin Med Phys,2022

5. A practical implementation of risk management for the clinical introduction of online adaptive Magnetic Resonance-guided radiotherapy;Klüter;Phys Imaging Radiat Oncol,2021

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