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Results for "deep reinforcement": 4 found

Failure Mode and Effects Analysis (FMEA) on Use of Surface Guided Imaging

Authors: Victoria Noelle Bry, Tamara Egan, Eric C. Ford, Angelia Landers, Juergen Meyer

Affiliation: Fred Hutch Cancer Center, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, University of Washington

Abstract Preview: Purpose: Surface guided radiation therapy (SGRT) can improve patient safety, however, its complex integration may expose processes to increased risk of error. This work identifies potential failures f...

Optimizing Fractionation Schedules for De-Escalation Radiotherapy in Head and Neck Cancers Using Deep Reinforcement Learning

Authors: Zhongjie Lu

Affiliation: Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine

Abstract Preview: Purpose: Patients with locally-advanced head and neck squamous cell carcinomas(HNSCCs), particularly those related to human papillomavirus(HPV), often achieve good locoregional control(LRC), yet they ...

Patient-Specific Deep Reinforcement Learning Framework for Automatic Replanning in Proton Therapy for Head-and-Neck Cancer

Authors: Malvern Madondo, Mark McDonald, Zhen Tian, Christopher Valdes, Ralph Weichselbaum, Xiaofeng Yang, David Yu, Jun Zhou

Affiliation: Department of Radiation & Cellular Oncology, University of Chicago, University of Chicago, Emory University, Department of Radiology, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose: Head-and-neck (HN) cancer patients often experience significant anatomical changes during treatment course. Proton therapy, particularly intensity-modulated proton therapy (IMPT), is sensitiv...

Scoring Functions for Reinforcement Learning in Accelerated Partial Breast Irradiation Treatment Planning

Authors: Rafe A. McBeth, Kuancheng Wang, Ledi Wang

Affiliation: Department of Radiation Oncology, University of Pennsylvania, Georgia Institute of Technology, University of Pennsylvania

Abstract Preview: Purpose:
The integration of AI in clinical workflows presents unprecedented opportunities to enhance treatment quality in radiation oncology, yet it also demands innovative approaches to address th...