Machine Intelligence in Imaging for Treatment Response Assessment and Prediction: Implications for Adaptive Radiation Therapy
J Wang1*, S Mattonen2*, W Yang3*, (1) UT Southwestern Medical Center, Dallas, TX, (2) Western University, London, ON, CA, (3) University of Southern California, Los Angeles, CA
Presentations
WE-D-BRC-0 (Wednesday, 7/13/2022) 10:15 AM - 11:15 AM [Eastern Time (GMT-4)]
Ballroom C
Artificial intelligence (AI) is bringing a paradigm shift in oncology, powered by the increasing availability of healthcare data and rapidly growing analytics techniques. Radiation therapy (RT) heavily depends on imaging technologies and image data generated pre, during, and post RT. The development of AI has played an important role in harnessing and analyzing imaging data for RT. AI is especially useful for adaptive RT (ART), in which a large amount of longitudinal image data are generated. In the session, we invite experts in the field to share their insights on this exciting topic. Selected disease sites, including head and neck, lung, and brain, will be discussed.
Learning Objectives:
1. Understand the basic concepts of AI-driven medical image analysis
2. Receive a broad review of AI for treatment response assessment and prediction
3. Understand the issues related to AI application in ART
Funding Support, Disclosures, and Conflict of Interest: NIH R01 EB029088, NIH R21CA234637
Handouts
- 175-63177-16291659-184035-73485788.pdf (Jing Wang)
- 175-63178-16291659-186693-689007252.pdf (Sarah Mattonen)
- 175-63179-16291659-184088-1136372503.pdf (Wensha Yang)
Keywords
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Taxonomy
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