Automating High Quality RT Planning at Scale

2025-01-21

AI Summary

Radiotherapy treatment planning faces three major bottlenecks: time-consuming manual workflows, poor inter-planner consistency, and scarcity of high-quality, standardized training data. To address these, we propose the Auto-Iterative Radiotherapy Planning (AIRTP) system—a fully automated, clinically guided framework that performs organ-at-risk (OAR) segmentation, auxiliary structure generation, beam configuration, dose optimization, and plan quality enhancement, with deep integration into the Varian Eclipse platform. Our method uniquely maps 3D dose predictions to physically feasible, clinically executable treatment plans. We introduce the largest publicly available, multi-cancer, standardized radiotherapy planning dataset to date—over ten times larger than prior benchmarks. AIRTP synergistically integrates deep learning (for OAR segmentation and dose prediction), inverse dose-to-parameter mapping, GDP-HMM modeling, and Eclipse API-driven optimization. Generated plans match expert quality and are produced in hours per case. We release nine clinical cohorts comprising >1,000 high-fidelity plans, supporting the AAPM 2025 Challenge.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances in artificial intelligence (AI) promise to improve its precision, efficiency, and consistency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning (AIRTP) system, a scalable solution for generating high-quality treatment plans. This scalable solution is designed to generate substantial volumes of consistently high-quality treatment plans, overcoming a key obstacle in the advancement of AI-driven RT planning. Our AIRTP pipeline adheres to clinical guidelines and automates essential steps, including organ-at-risk (OAR) contouring, helper structure creation, beam setup, optimization, and plan quality improvement, using AI integrated with RT planning software like Eclipse of Varian. Furthermore, a novel approach for determining optimization parameters to reproduce 3D dose distributions, i.e. a method to convert dose predictions to deliverable treatment plans constrained by machine limitations. A comparative analysis of plan quality reveals that our automated pipeline produces treatment plans of quality comparable to those generated manually, which traditionally require several hours of labor per plan. Committed to public research, the first data release of our AIRTP pipeline includes nine cohorts covering head-and-neck and lung cancer sites to support an AAPM 2025 challenge. This data set features more than 10 times the number of plans compared to the largest existing well-curated public data set to our best knowledge. Repo:{https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge}

Problem

• Radiation Therapy Planning
• Time-consuming Process
• AI Training Data Standardization

Innovation

• AIRTP System
• AI-assisted Treatment Planning
• Large-scale Datasets