用AI自动生成高质量放疗计划，解决数据稀缺难题。
Automating RT Planning at Scale: High Quality Data For AI Training
- 构建自动化放疗规划系统，整合多步骤AI流程。
- 生成超10倍于现有公开数据集的高质量计划。
- 适合放疗AI研究者参与2025年AAPM挑战赛。
放疗规划复杂且耗时，AI发展受限于高质量数据不足。本文提出自动化迭代放疗规划（AIRTP）系统，可规模化生成一致高质量治疗计划。该系统遵循临床指南，自动完成器官保护区勾画、辅助结构创建、束流设置、优化及计划质量提升，集成于Varian Eclipse等放疗软件中。提出新方法将剂量预测转换为满足设备限制的可执行计划，实现3D剂量分布复现。对比显示，自动化生成计划质量与人工规划相当，后者通常需数小时/例。首个数据发布包含9个队列，覆盖头颈与肺癌，支持AAPM 2025挑战赛。据知，该数据集计划数量超过现有最大公开数据集10倍以上。代码开源：https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge。
原文摘要 · Abstract (English)
Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, 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 Varian Eclipse. 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 is proposed. 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. To our best knowledge, this dataset features more than 10 times number of plans compared to the largest existing well-curated public dataset. Repo: https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge.
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