Kavli Affiliate: Jing Wang
| First 5 Authors: Elahheh Salari, Jing Wang, Jacob Wynne, Chih-Wei Chang, Xiaofeng Yang
| Summary:
Radiotherapy aims to deliver a prescribed dose to the tumor while sparing
neighboring organs at risk (OARs). Increasingly complex treatment techniques
such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery
(SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been
developed to deliver doses more precisely to the target. While such
technologies have improved dose delivery, the implementation of intra-fraction
motion management to verify tumor position at the time of treatment has become
increasingly relevant. Recently, artificial intelligence (AI) has demonstrated
great potential for real-time tracking of tumors during treatment. However,
AI-based motion management faces several challenges including bias in training
data, poor transparency, difficult data collection, complex workflows and
quality assurance, and limited sample sizes. This review serves to present the
AI algorithms used for chest, abdomen, and pelvic tumor motion
management/tracking for radiotherapy and provide a literature summary on the
topic. We will also discuss the limitations of these algorithms and propose
potential improvements.
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