Konrad Maximilian Lukas, Brink Carsten, Bertelsen Anders Smedegaard, Lorenzen Ebbe Laugaard, Celik Bahar, Nyborg Christina Junker, Dysager Lars, Schytte Tine, Bernchou Uffe
Department of Clinical Research, University of Southern Denmark, Denmark.
Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Denmark.
Phys Imaging Radiat Oncol. 2024 Dec 28;33:100694. doi: 10.1016/j.phro.2024.100694. eCollection 2025 Jan.
Daily magnetic resonance image (MRI)-guided radiotherapy plan adaptation requires time-consuming manual contour edits of targets and organs at risk in the online workflow. Recent advances in auto-segmentation promise to deliver high-quality delineations within a short time frame. However, the actual time benefit in a clinical setting is unknown. The current study investigated the feasibility and time gain of implementing online artificial intelligence (AI)-based delineations at a 1.5 T MRI-Linac.
Fifteen consecutive prostate cancer patients, treated to 60 Gy in 20 fractions at a 1.5 T MRI-Linac, were included in the study. The first 5 patients (Group 1) were treated using the standard contouring workflow for all fractions. The last 10 patients (Group 2) were treated with the standard workflow for fractions 1 up to 3 (Group 2 - Standard) and an AI-based workflow for the remaining fractions (Group 2 - AI). AI delineations were delivered using an in-house developed AI inference service and an in-house trained nnU-Net.
The AI-based workflow reduced delineation time from 9.8 to 5.3 min. The variance in delineation time seemed to increase during the treatment course for Group 1, while the delineation time for the AI-based workflow was constant (Group 2 - AI). Fewer occurrences of readaptation due to target movement occurred with the AI-based workflow.
Implementing an AI-based workflow at the 1.5 T MRI-Linac is feasible and reduces the delineation time. Lower variance in delineation duration supports a better ability to plan daily treatment schedules and avoids delays.
每日磁共振成像（MRI）引导的放射治疗计划调整需要在在线工作流程中对靶区和危及器官进行耗时的手动轮廓编辑。自动分割技术的最新进展有望在短时间内提供高质量的轮廓描绘。然而，在临床环境中的实际时间效益尚不清楚。本研究调查了在1.5T MRI直线加速器上实施基于在线人工智能（AI）的轮廓描绘的可行性和时间增益。
本研究纳入了15例连续的前列腺癌患者，在1.5T MRI直线加速器上接受20次分割、总剂量60Gy的治疗。前5例患者（第1组）在所有分割中均采用标准轮廓描绘工作流程。后10例患者（第2组）在第1至3次分割中采用标准工作流程（第2组 - 标准），其余分割采用基于AI的工作流程（第2组 - AI）。使用内部开发的AI推理服务和内部训练的nnU-Net进行AI轮廓描绘。
基于AI的工作流程将轮廓描绘时间从9.8分钟减少到5.3分钟。第1组在治疗过程中轮廓描绘时间的差异似乎增加，而基于AI的工作流程的轮廓描绘时间保持恒定（第2组 - AI）。基于AI的工作流程因靶区移动而进行重新调整的情况较少。
在1.5T MRI直线加速器上实施基于AI的工作流程是可行的，并可减少轮廓描绘时间。轮廓描绘持续时间的较低差异有助于更好地规划每日治疗计划并避免延误。
