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.

翻译：放射治疗（RT）计划制定过程复杂、主观性强且耗时。人工智能（AI）的进步有望提升其精度与效率，但发展常受限于大规模标准化数据集的稀缺。为此，我们提出了自动化迭代放疗计划（AIRTP）系统，这是一种可扩展的解决方案，用于生成高质量治疗计划。该可扩展方案旨在产生大量持续高质量的治疗计划，从而克服AI驱动放疗计划发展的关键障碍。我们的AIRTP流程遵循临床指南，并自动化了关键步骤，包括危及器官（OAR）轮廓勾画、辅助结构创建、射束设置、优化及计划质量提升，通过将AI与Varian Eclipse等放疗计划软件集成实现。此外，我们提出了一种确定优化参数以复现三维剂量分布的新方法，即一种将剂量预测转化为受机器限制的可执行治疗计划的技术。计划质量的对比分析表明，我们的自动化流程生成的治疗计划质量与人工制定的计划相当，而传统人工制定每个计划通常需要数小时工作量。为支持公共研究，我们AIRTP流程的首批数据发布包含九个队列，涵盖头颈部和肺癌部位，以支持AAPM 2025挑战赛。据我们所知，该数据集包含的计划数量是现有最大精心策划公共数据集的10倍以上。代码库：https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge。