Autonomous Radiotherapy Planning

The challenge in cancer treatment lies in the precise delivery of radiation therapy, which uses high-energy rays to destroy cancer cells. As patients undergo treatment, their bodies can change in ways that influence how radiation should be administered. Tumors may shrink, and organs might shift slightly, creating a dynamic landscape that requires real-time adjustments in radiation delivery. Adjusting treatment plans to accommodate these changes could significantly enhance the efficacy and safety of therapy. However, the current treatment planning systems (TPS) used in radiation therapy come with limitations. One major issue is the slow adaptation process. These systems are not fast or automated enough to keep pace with the changes occurring in the patient`s body. Although advancements in imaging technologies and radiation machines have been made, the planning tools fail to fully leverage these innovations. Consequently, there are missed opportunities to improve patient care during treatment. Our project aims to address these challenges by developing a new system that harnesses advanced artificial intelligence (AI) to make radiation therapy more personalized, efficient, and adaptable in real time. The cornerstone of our approach is the creation of a fully automated treatment planning system, which operates independently of the traditional software currently in use. This innovative system will utilize Deep Learning (DL), a type of AI that learns from vast amounts of data, to generate treatment plans automatically, without the need for manual input. Additionally, we are exploring the potential of Reinforcement Learning (RL), another AI method where the system learns through trial and error to discover the best course of action. RL will help the AI fine-tune the settings of radiation machines, allowing them to target cancer cells more effectively while minimizing harm to surrounding healthy tissues. Another key element of our solution is the development of a real-time adaptive workflow. Our goal is to establish a process that enables treatment plans to adjust instantly as changes in the patients anatomy are detected. This approach ensures that each radiation session is optimized for the patients current condition, leading to more effective and safer treatment outcomes. Why does this matter? First and foremost, this system promises to improve patient care by delivering radiation therapy that adapts quickly to anatomical changes, allowing for more precise targeting of cancer cells and reducing side effects. Moreover, by automating the planning process, we free up valuable time for doctors and technicians, enabling them to focus more on patient care rather than the tedious task of manual planning. Finally, by streamlining and automating the treatment planning process, we can help more medical centers offer advanced radiation therapy, ultimately benefiting patients in more locations.