Modern radiation oncology is poised for a significant breakthrough, focusing on delivering personalized care by adapting treatments in real-time to patients' anatomical changes. While advancements in image-guided systems and delivery techniques have paved the way, the full potential of real-time adaptive strategies remains untapped due to limitations in automation and speed within current Treatment Planning Systems (TPS).

Our groundbreaking project aims to overcome these challenges by integrating cutting-edge Deep Learning (DL) and Reinforcement Learning (RL) technologies into the treatment planning process. We have three core objectives:

⦁ Pioneer a Fully Autonomous Treatment Planning Pipeline: Develop the world's first radiation therapy planning system that operates independently of conventional TPS, leveraging advanced DL architectures.

⦁ Explore Reinforcement Learning in Treatment Planning: Investigate how RL can enhance the planning process by optimizing machine parameters in radiotherapy plans

⦁ Establish a Next-Generation Workflow: Create a real-time adaptive treatment workflow that significantly improves the effectiveness and safety of radiation therapy.