Department of Radiation Oncology, CONFIAR Radiotherapy, Goiânia, Goiás, Brazil.
Department of Radiation Oncology, University Hospital of Brasilia, Brasilia, Federal District, Brazil.
J Appl Clin Med Phys. 2024 May;25(5):e14361. doi: 10.1002/acm2.14361. Epub 2024 Apr 20.
This study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics.
We retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System. We employed three beam insertion geometries and three optimization strategies, resulting in 3600 plans, each delivering a 40.05 Gy dose in 15 fractions. Gantry angles in the tangent fields were selected based on a criterion involving the minimum intersection area between the Planning Target Volume (PTV) and the ipsilateral lung in the Beam's Eye View projection. ESAPI was also used to gather patient anatomical data, serving as input for Random Forest models to select the optimal plan. The Random Forest classification considered both beam insertion geometry and optimization strategy. Dosimetric data were evaluated in accordance with the Radiation Therapy Oncology Group (RTOG) 1005 protocol.
Overall, all approaches generated high-quality plans, with approximately 94% meeting the acceptable dose criteria for organs at risk and/or target coverage as defined by RTOG guidelines. Average automated plan generation time ranged from 6 min and 37 s to 9 min and 22 s, with the mean time increasing with additional fields. The Random Forest approach did not successfully enable automatic planning strategy selection. Instead, our automated planning system allows users to choose from the tested geometry and strategy options.
Although our attempt to correlate patient anatomical features with planning strategy using machine learning tools was unsuccessful, the resulting dosimetric outcomes proved satisfactory. Our algorithm consistently produced high-quality plans, offering significant time and efficiency advantages.
本研究旨在开发和验证用于乳腺癌患者调强放射治疗（IMRT）计划自动化的算法，重点关注患者的解剖学特征。
我们回顾性选择了 400 例无淋巴结受累的乳腺癌患者进行自动治疗计划。自动化是通过 Eclipse 脚本应用程序编程接口（ESAPI）在 Eclipse 治疗计划系统中实现的。我们使用了三种射束插入几何形状和三种优化策略，产生了 3600 个计划，每个计划在 15 个分次中给予 40.05Gy 的剂量。切线野的机架角度是根据涉及计划靶区（PTV）和 Beam's Eye View 投影中同侧肺之间最小交叠面积的标准选择的。ESAPI 还用于收集患者解剖学数据，作为输入用于随机森林模型选择最佳计划。随机森林分类考虑了射束插入几何形状和优化策略。剂量学数据是根据放射肿瘤学组（RTOG）1005 协议进行评估的。
总的来说，所有方法都生成了高质量的计划，大约 94%的计划符合 RTOG 指南定义的危及器官和/或靶区覆盖的可接受剂量标准。自动计划生成的平均时间从 6 分 37 秒到 9 分 22 秒不等，随着附加野的增加，平均时间增加。随机森林方法未能成功实现自动规划策略选择。相反，我们的自动化计划系统允许用户从测试的几何形状和策略选项中进行选择。
尽管我们尝试使用机器学习工具将患者的解剖学特征与规划策略相关联，但没有成功，但所得的剂量学结果令人满意。我们的算法始终生成高质量的计划，具有显著的时间和效率优势。
