目的:放射治疗中的人工智能(AI)模型正在以越来越快的速度发展。尽管如此,放射治疗界尚未在临床实践中广泛采用这些模型。关于如何发展的有凝聚力的指导方针,报告和临床验证AI算法可能有助于弥合这一差距。
方法:遵循所有合著者的Delphi过程,以确定在此综合指南中应该解决哪些主题。指南的单独部分,包括语句,由作者的小组撰写,并在几次会议上与整个小组进行了讨论。陈述被制定并被评分为高度推荐或推荐。
结果:发现以下主题最相关:决策,图像分析,体积分割,治疗计划,患者特定的治疗质量保证,适应性治疗,结果预测,培训,AI模型参数的验证和测试,模型可用性供其他人验证,模型质量保证/更新和升级,道德。给出了关键参考文献,并展望了当前的障碍和克服这些障碍的可能性。编写了19份声明。
结论:已经编写了一个有凝聚力的指南,该指南涉及放射治疗中有关AI的主要主题。有助于指导发展,以及新AI工具的透明和一致的报告和验证,并促进采用。
OBJECTIVE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive
guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.
METHODS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive
guideline. Separate sections of the
guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended.
RESULTS: The following topics were found most relevant: Decision making, image analysis, volume
segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated.
CONCLUSIONS: A cohesive
guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.