Real-Time AI-Based Radiotherapy Planning for Nasopharyngeal Carcinoma: Development and Validation

Online all-in-one (AIO) radiotherapy workflows enable same-day treatment by integrating simulation, planning, and delivery into a single session. However, for anatomically complex tumors such as nasopharyngeal carcinoma (NPC), generating high-quality plans within strict time constraints remains a major barrier to clinical adoption.

We developed a deep-learning-based automated planning model specifically tailored for real-time NPC planning in the online AIO workflow. The model was trained on 890 patients and iteratively refined through 4 versions, incorporating innovations such as quantile loss, priority-based constraint encoding, and hybrid central processing unit–graphics processing unit acceleration. Model performance was benchmarked in a 5-center retrospective study, including 125 patients from the model development center and 120 patients from 4 external centers. It was then prospectively validated in 242 consecutively treated patients with NPC using a CT-linear accelerator-based AIO platform.

In the 5-center retrospective evaluation, artificial intelligence (AI)-generated plans achieved superior or comparable dosimetric quality relative to expert manual plans, despite variations in imaging, contouring, and prescription practices. In prospective deployment, 95% of plans were clinically accepted after a single optimization cycle, with a mean generation time of 3.5 min. All plans met target coverage criteria and passed both secondary dose verification and in vivo electronic portal imaging device analysis.

This study represents the largest prospective validation to date of AI-based treatment planning for NPC, demonstrating real-time feasibility, robust generalizability, and consistent clinical quality. Our development-to-deployment framework supports the scalable adoption of AI-driven precision planning and provides a transferable model for intelligent radiotherapy across disease sites.