Autonomous radiotherapy planning via agentic orchestration using a multimodal TPS-integrated compound AI platform.

内容摘要

Purpose. Radiation therapy (RT) treatment planning requires iterative, multi-day optimization workflows in which subjective planning strategies produce inter-planner variability in plan quality. Existing computational approaches automate isolated aspects of this workflow, yet none orchestrates an end-to-end pipeline from physician directive to deliverable plan. We developed a compound artificial intelligence (AI) platform for fully autonomous RT treatment planning that combines multi-agent large language model (LLM) orchestration with directive-conditioned three-dimensional (3D) dose prediction, natively integrated with a commercial treatment planning system (TPS). Methods. Seven specialized agents navigated the multi-objective optimization landscape through structured clinical reasoning, iteratively analyzing dose-volume histogram (DVH) metrics and spatial dose patterns, formulating trade-off strategies, and executing validated modifications through the TPS across five fully autonomous iterations per case. A directive-conditioned 3D dose prediction model supplied patient-specific DVH values from which initial optimization objectives were autonomously derived, eliminating the need for curated templates or manual initialization. A retrieval-augmented generation (RAG) system encoded institutional knowledge into the planning workflow. We evaluated 60 retrospective cases across brain, lung, and prostate sites, with 10 intensity-modulated RT (IMRT) and 10 volumetric modulated arc therapy (VMAT) plans per site spanning 20.0-79.2 Gy in 3-44 fractions, scored by the proportion of dosimetric criteria satisfied. Results. Across all 60 cases, AI plans achieved 89.8 &#xb1; 9.4% of dosimetric criteria versus 85.2 &#xb1; 10.8% for clinical reference plans ( p < 0.001 ). IMRT plans improved in 25 of 30 cases with none worsened (94.1 &#xb1; 6.7% vs 84.3 &#xb1; 11.8%, p < 0.001 ); VMAT plans showed no significant difference (85.6 &#xb1; 9.9% vs 86.1 &#xb1; 9.7%, p = 0.770 ). Each plan iteration completed in 20.2 &#xb1; 12.7 min, of which agent reasoning consumed 5.2 &#xb1; 1.7 min ( 114,429 &#xb1; 11,798 tokens, 0.43 &#xb1; 0.04 ). Conclusions. These results established the feasibility of end-to-end, fully autonomous, universal RT treatment planning through compound AI. Integrating dose prediction as an agent-invoked tool for objective initialization resolved the dependency on curated templates and manual specification that constrained prior LLM-based planning systems.