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:{this https URL}
Abstract (translated)
放射治疗（RT）计划制定是一个复杂、主观且耗时的过程。人工智能（AI）的进步承诺能提高其精确度、效率和一致性，但进展常常受限于大型标准化数据集的稀缺性。为了解决这一问题，我们引入了自动迭代放疗规划（AIRTP）系统，这是一种可扩展解决方案，用于生成高质量治疗计划。该可扩展解决方案旨在产生大量一致且高质量的治疗方案，从而克服AI驱动RT规划中的一大障碍。 我们的AIRTP流程遵循临床指南，并自动化了许多关键步骤，包括风险器官轮廓勾画、辅助结构创建、射束布置、优化和改善计划质量等环节，这些操作通过集成到如Varian公司Eclipse放射治疗软件中的AI技术来实现。此外，我们还提出了一种确定优化参数的方法，以重现三维剂量分布，并将其转换为受机器限制的可实施治疗方案。 在治疗计划的质量比较分析中发现，我们的自动化流程产生的治疗计划质量与手动生成的计划相当，而后者通常需要每份计划数小时的人力劳动。致力于公共研究，我们AIRTP流程的第一个数据发布包括九个队列，涵盖了头颈部和肺癌部位，并支持AAPM 2025挑战赛。此数据集包含的治疗方案数量是我们目前所知的最大、最完善且公开的数据集中同类数据的十倍以上。 相关代码库地址：[点击这里](https://this https URL)（请将URL替换为实际链接）
URL
https://arxiv.org/abs/2501.11803
