Cardenas Carlos E, Cardan Rex A, Harms Joseph, Simiele Eric, Popple Richard A
Department of Radiation Oncology, University of Alabama at Birmingham, AL, USA.
Department of Radiation Oncology, University of Alabama at Birmingham, AL, USA.
Radiother Oncol. 2025 Jan;202:110598. doi: 10.1016/j.radonc.2024.110598. Epub 2024 Oct 28.
The ESTRO 2023 Physics Workshop hosted the Fully-Automated Radiotherapy Treatment Planning (Auto-RTP) Challenge, where participants were provided with CT images from 16 prostate cancer patients (6 prostate only, 6 prostate + nodes, and 4 prostate bed + nodes) across 3 challenge phases with the goal of automatically generating treatment plans with minimal user intervention. Here, we present our team's winning approach developed to swiftly adapt to both different contouring guidelines and treatment prescriptions than those used in our clinic.
Our planning pipeline comprises two main components: 1) auto-contouring and 2) auto-planning engines, both internally developed and activated via DICOM operations. The auto-contouring engine employs 3D U-Net models trained on a dataset of 600 prostate cancer patients for normal tissues, 253 cases for pelvic lymph node, and 32 cases for prostate bed. The auto-planning engine, utilizing the Eclipse Scripting Application Programming Interface, automates target volume definition, field geometry, planning parameters, optimization, and dose calculation. RapidPlan models, combined with multicriteria optimization and scorecards defined on challenge scoring criteria, were employed to ensure plans met challenge objectives. We report leaderboard scores (0-100, where 100 is a perfect score) which combine organ-at-risk and target dose-metrics on the provided cases.
Our team secured 1st place across all three challenge phases, achieving leaderboard scores of 79.9, 77.3, and 78.5 outperforming 2nd place scores by margins of 6.4, 0.4, and 2.9 points for each phase, respectively. Highest plan scores were for prostate only cases, with an average score exceeding 90. Upon challenge completion, a "Plan Only" phase was opened where organizers provided contours for planning. Our current score of 90.0 places us at the top of the "Plan Only" leaderboard.
Our automated pipeline demonstrates adaptability to diverse guidelines, indicating progress towards fully automated radiotherapy planning. Future studies are needed to assess the clinical acceptability and integration of automatically generated plans.
欧洲放射肿瘤学会（ESTRO）2023年物理研讨会举办了全自动放射治疗治疗计划（Auto-RTP）挑战赛，为参与者提供了来自16例前列腺癌患者（6例仅前列腺、6例前列腺+淋巴结、4例前列腺床+淋巴结）的CT图像，分3个挑战阶段，目标是在最少用户干预的情况下自动生成治疗计划。在此，我们展示我们团队开发的获胜方法，该方法能迅速适应与我们临床使用的不同的轮廓勾画指南和治疗处方。
我们的计划流程包括两个主要部分：1）自动轮廓勾画和2）自动计划引擎，均为内部开发并通过DICOM操作激活。自动轮廓勾画引擎采用在600例前列腺癌患者数据集上训练的3D U-Net模型，用于正常组织的有253例盆腔淋巴结的和32例前列腺床的。自动计划引擎利用Eclipse脚本应用程序编程接口，自动进行靶区体积定义、射野几何形状、计划参数、优化和剂量计算。采用快速计划模型，并结合基于挑战评分标准定义的多标准优化和计分卡，以确保计划符合挑战目标。我们报告在提供的病例上结合危及器官和靶区剂量指标的排行榜分数（0-100，100分为满分）。
我们团队在所有三个挑战阶段均获得第一名，排行榜分数分别为79.9、77.3和78.5，分别比每个阶段的第二名分数高出6.4、0.4和2.9分。最高计划分数是仅前列腺病例的，平均分数超过90分。挑战赛结束后，开启了一个“仅计划”阶段，组织者提供用于计划的轮廓。我们目前90.0的分数使我们在“仅计划”排行榜上名列前茅。
我们的自动化流程展示了对不同指南的适应性，表明在实现全自动放射治疗计划方面取得了进展。未来需要进行研究以评估自动生成计划的临床可接受性和整合情况。
