AI End-to-End Radiation Treatment Planning Under One Second
This work significantly reduces radiation treatment planning time from minutes to under one second, improving efficiency and consistency for clinical radiation oncology workflows.
The paper introduces AIRT, an end-to-end deep-learning framework that generates single-arc VMAT prostate radiation treatment plans in under one second. It achieves non-inferiority to RapidPlan Eclipse in target coverage and OAR sparing, with target homogeneity (HI = 0.10 ± 0.01) similar to reference plans.
Artificial intelligence-based radiation therapy (RT) planning has the potential to reduce planning time and inter-planner variability, improving efficiency and consistency in clinical workflows. Most existing automated approaches rely on multiple dose evaluations and corrections, resulting in plan generation times of several minutes. We introduce AIRT (Artificial Intelligence-based Radiotherapy), an end-to-end deep-learning framework that directly infers deliverable treatment plans from CT images and structure contours. AIRT generates single-arc VMAT prostate plans, from imaging and anatomical inputs to leaf sequencing, in under one second on a single Nvidia A100 GPU. The framework includes a differentiable dose feedback, an adversarial fluence map shaping, and a plan generation augmentation to improve plan quality and robustness. The model was trained on more than 10,000 intact prostate cases. Non-inferiority to RapidPlan Eclipse was demonstrated across target coverage and OAR sparing metrics. Target homogeneity (HI = 0.10 $\pm$ 0.01) and OAR sparing were similar to reference plans when evaluated using AcurosXB. These results represent a significant step toward ultra-fast standardized RT planning and a streamlined clinical workflow.