Treatment Planning

Treatment planning in radiotherapy aims to optimize radiation delivery, maximizing tumor coverage while minimizing damage to healthy organs. Current research heavily utilizes deep learning, employing architectures like UNet, transformers, and diffusion models, to automate tasks such as organ-at-risk segmentation, dose prediction, and treatment plan optimization. These advancements leverage multi-modal data (CT, MRI) and incorporate uncertainty estimation to improve accuracy and reliability, ultimately leading to more efficient and effective cancer treatment. The resulting improvements in precision and speed have significant implications for patient care and resource allocation within radiation oncology.