将间接的MRI信息纳入基于CT的深度学习模型,用于前列腺自身细分
Daan Stas1, Geert De Kerf1, Michaël Claessens2
1Department of Radiation Oncology, Iridium Network, Antwerp, Belgium; Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
概括
一个新的深度学习模型在CT扫描上准确地描述前列腺癌结构,遵循基于MRI的指导方针. 这个人工智能工具通过提高轮准确度而有助于放射治疗规划,而不需要直接MRI输入.
科学领域:
- 辐射疗法 辐射疗法
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 计算机断层扫描 (CT) 成像在为前列腺癌划分软组织方面存在挑战.
- 目前的指导方针要求整合磁共振成像 (MRI) 信息以获得准确的轮.
- 准确的风险器官划分对于有效的前列腺癌放射治疗至关重要.
研究的目的:
- 开发和评估深度学习 (DL) 模型,用于CT图像上的前列腺和风险器官轮.
- 该模型旨在直接从CT数据中复制基于MRI的轮精度.
- 与欧洲放射治疗和瘤学会 (ESTRO) 和辐射瘤实践咨询委员会 (ACROP) 的指导方针保持一致.
主要方法:
- 利用了来自165名前列腺癌患者的CT扫描数据 (136名用于培训,29名用于测试).
- 开发了一个3D U-Net深度学习架构,用于轮关键区域:临床目标体积 (前列腺有/没有静脉),膀,阳肠和精液囊.
- 临床医生进行了定性审查,并使用子相似系数 (DSC) 和豪斯多夫距离 (HD95) 与手动划分进行了定量比较.
主要成果:
- DL模型在前列腺目标体积 (CTV-iVP,CTV-eVP) 中获得了高质量评分,需要临床医生最小调整 (96%).
- 定量分析显示,CTV-iVP和CTV-eVP的性能可比,DSC为89%,HD95为4mm.
- 该模型对膀 (DSC 96%,HD95 2.9mm) 显示出强的结果,对直肠和精液囊泡显示出可接受的性能.
结论:
- 这是第一个在CT成像中实施MRI轮指南的DL模型.
- 基于CT的DL模型是根据ESTRO-ACROP指南进行训练的,这代表了一种新的方法.
- 开发的模型是放射治疗中前列腺划分的宝贵工具,减少了对直接MRI输入的依赖.
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