In 2025, 317,000 new breast cancer cases will be diagnosed in the United States with 24,000 of those cases in Texas. Consequently, breast cancer patients account for 20-50% of the caseload at community radiotherapy clinics. The preparation of a radiotherapy treatment plan involves identifying and contouring structures on a CT image, including both the normal tissues to avoid and the targeted treatment areas, as well as positioning and optimization of radiotherapy beams. This takes hours of dedicated time and effort from both the radiation oncologist and treatment planner and can be subject to quality variations between practitioners. It can easily take more than a week from receiving a CT sc...
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In 2025, 317,000 new breast cancer cases will be diagnosed in the United States with 24,000 of those cases in Texas. Consequently, breast cancer patients account for 20-50% of the caseload at community radiotherapy clinics. The preparation of a radiotherapy treatment plan involves identifying and contouring structures on a CT image, including both the normal tissues to avoid and the targeted treatment areas, as well as positioning and optimization of radiotherapy beams. This takes hours of dedicated time and effort from both the radiation oncologist and treatment planner and can be subject to quality variations between practitioners. It can easily take more than a week from receiving a CT scan to starting treatment. Artificial intelligence has been shown to be capable of creating high quality radiation plans in a very short time leading to reduced time between CT imaging and starting treatment, as well as providing consistently higher quality planning than many patients currently have access to. This may also reduce hospital visits and hotel stays, particularly for patients who must travel some distance from their home to receive care. This travel is a significant hurdle for cancer patients in rural areas - for example, a significant portion of Texas is a radiotherapy desert and individuals residing in these areas must travel more than 50 miles to the nearest facility However, AI may introduce unexpected errors into the treatment plans. Additionally, because less people are involved in the overall process, our ability to catch errors may be reduced. In this study, we will prospectively evaluate the use of AI-based radiotherapy planning for breast cancer, including the use of AI-based tools to support the process of catching errors. Throughout, we will focus on risk and risk mitigation. The outcome of this study will be a full understanding of how to safely deploy AI planning such that patients can benefit from the advantages of AI, without additional risk.
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