Vandewinckele Liesbeth, Benazzouz Chahrazad, Delombaerde Laurence, Pape Laure, Reynders Truus, Van der Vorst Aline, Callens Dylan, Verstraete Jan, Baeten Adinda, Weltens Caroline, Crijns Wouter
Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium.
Department of Radiation Oncology, UZ Leuven, Belgium.
Phys Imaging Radiat Oncol. 2024 Nov 22;32:100677. doi: 10.1016/j.phro.2024.100677. eCollection 2024 Oct.
With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy.
The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS). A proactive Failure Mode and Effect Analysis (FMEA) was conducted by a multidisciplinary team, including physicians, physicists, dosimetrists, technologists, quality managers, and the research and development team. Failure modes categorised as 'Not acceptable' and 'Tolerable' on the risk matrix were further examined to find mitigation strategies.
In total, 39 failure modes were defined for the total workflow, divided over four steps. Of these, 33 were deemed 'Acceptable', five 'Tolerable', and one 'Not acceptable'. Mitigation strategies, such as a case-specific Quality Assurance report, additional scripted checks and properties, a pop-up window, and time stamp analysis, reduced the failure modes to two 'Tolerable' and none in the 'Not acceptable' region.
The pro-active risk analysis revealed possible risks in the implemented workflow and led to the implementation of mitigation strategies that decreased the risk scores for safer clinical use.
随着放射治疗领域内部创建的深度学习模型数量不断增加，在临床应用之前了解如何将与局部临床实施相关的风险降至最低至关重要。本研究的目的是给出一个示例，说明如何在包含基于深度学习的乳腺容积调强弧形治疗计划工具的已实施工作流程中识别风险并找到减轻这些风险的策略。
深度学习模型在私有谷歌云环境中运行以获得足够的计算能力，并集成到可在临床治疗计划系统（TPS）内启动的工作流程中。一个跨学科团队，包括医生、物理学家、剂量师、技术人员、质量经理以及研发团队，进行了前瞻性失效模式与效应分析（FMEA）。对风险矩阵中分类为“不可接受”和“可容忍”的失效模式进行了进一步研究，以找到减轻风险的策略。
整个工作流程共定义了39种失效模式，分为四个步骤。其中，33种被认为“可接受”，5种“可容忍”，1种“不可接受”。通过特定病例质量保证报告、额外的脚本检查和属性、弹出窗口以及时间戳分析等减轻风险策略，将失效模式减少到2种“可容忍”，且“不可接受”区域内没有失效模式。
前瞻性风险分析揭示了已实施工作流程中可能存在的风险，并促使实施减轻风险策略，从而降低风险评分，实现更安全的临床应用。
