Bakx Nienke, van der Sangen Maurice, Theuws Jacqueline, Bluemink Johanna, Hurkmans Coen
Department of Radiation Oncology, Catharina Hospital, Eindhoven, The Netherlands.
Faculties of Applied Physics and Electrical Engineering, Technical University Eindhoven, Eindhoven, The Netherlands.
Phys Imaging Radiat Oncol. 2023 Sep 27;28:100496. doi: 10.1016/j.phro.2023.100496. eCollection 2023 Oct.
Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners were allowed to manually adapt them. Plans were evaluated based on clinical goals and DVH parameters. Thirty-seven of 50plans did fulfill all clinical goals without adjustments. Thirteen of these 37 plans were still adjusted but did not improve mean heart or lung dose. These results leave room for improvement of both the DL model as well as education on clinically relevant adjustments.
深度学习（DL）模型越来越多地被用于自动化放射治疗计划制定过程。本研究评估了此类模型在全乳放疗中的临床应用。自动生成治疗计划后，计划制定者可以手动调整这些计划。根据临床目标和剂量体积直方图（DVH）参数对计划进行评估。50个计划中有37个无需调整即可满足所有临床目标。这37个计划中有13个仍进行了调整，但并未降低心脏或肺部的平均剂量。这些结果为改进DL模型以及临床相关调整的教育留出了空间。
