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Automation in Radiotherapy - Fasten Your Seatbelt!

M Cao1*, C Shi2*, K Younge3*, (1) UCLA School of Medicine, Los Angeles, CA, (2) Memorial Sloan Kettering Cancer Center, Basking Ridge, NJ, (3) University of Michigan, Ann Arbor, MI


(Tuesday, 7/31/2018) 7:30 AM - 8:30 AM

Room: Davidson Ballroom A

It has been well established that automation and hard safety barriers are the most effective safety methods for reducing errors. Recent advances in computer and information technology have great potential to improve the efficiency and quality of radiotherapy treatments through automation, which has the potential to significantly alter the practice of radiotherapy. This session is composed of three presentations reviewing and discussing current technologies available for automation of time-consuming and repetitive tasks in radiation oncology. The first presentation will focus on methods and technologies available for auto-segmentation of structure contours for treatment planning. The second presentation will discuss implementation of automation in the physics plan evaluation with a focus on incident learning system (ILS) driven automation priorities. This type of automation allows team members performing physics plan checks to focus more thoroughly on the manual aspects of plan QA, and wherever possible attempts to remove the human element that can lead to the creation and/or propagation of errors throughout the workflow chain. The final presentation will focus on automation of quality assurance of radiotherapy treatment machines. The benefits and potential risks of automation in these areas will be discussed with a review of the literature. Clinical implementation and practical considerations of these automated tools will be presented and discussed.

Learning Objectives:
1. To recognize the current and future role of automation in radiation oncology
2. To understand the benefits and risks associated with automation in different radiation oncology workflows
3. To understand practical considerations in implementing automation and how to best incorporate these techniques with existing clinical workflows



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