Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer types.

Abstract

Deep learning (DL) -based automated treatment planning (ATP) shows significant promise in streamlining radiotherapy workflow and reducing variability in plan quality. However, it often lacks the flexibility needed for achieving individualized trade-offs in real-world practice. Herein, we propose a hybrid strategy by integrating DL-based dose prediction with clinical-goal-guided inverse optimization to generate directly deliverable plans within five minutes. DL models for five disease sites were trained separately using datasets from a single institution and were tested retrospectively for clinical application among three institutions, with tailored prioritized clinical goals. We find that over 80% of the 250 auto-plans met clinical criteria, and 60% were preferred over manual plans in blinded reviews. Dosimetric analyses show that the auto-plans quantitatively matched or exceeded the quality of human-driven plans. This study highlights ATP's potential to transform radiotherapy practice, with ongoing efforts aimed at refining its versatility and adoption across diverse clinical settings.