文章：

基于人工智能器官分割与多通道圆柱体建模的阴道袖口高剂量率近距离放射治疗工作流程效率研究

Workflow Efficiency in Vaginal Cuff High Dose Rate Brachytherapy Using Artificial Intelligence-Based Organ Segmentation and Multi-Channel Cylinder Modeling

原文发布日期：23 August 2025

DOI: 10.3390/cancers17172751

类型: Article

开放获取: 是

英文摘要：

Background/Objectives: High dose rate brachytherapy (HDR-BT) is resource-intensive. Workflow efficiency and inter-user variability remain prevalent issues in HDR-BT. To improve workflow efficiency and reduce inter-user variability, we introduced artificial intelligence (AI)-based organ contouring (AC) and applicator modeling (AM) into our clinical workflow. Here, we present results on the impact of these tools on workflow efficiency, inter-user variability, and plan quality for vaginal cuff HDR-BT cases.Methods: 260 treated fractions were included in the analysis, half of which were treated before implementing AC and AM. Five different medical physicists performed the treatment planning. Using built-in timestamps, contouring, dose planning, and total treatment planning times were recorded for each fraction. Dosimetric data, including the dose to the highest 2cc (D2cc) of bladder and rectum, and percentage volumes of the target covered by 90–200% isodose lines (V90-200) were recorded. Analysis of variance with post-hoc Tukey tests were used to determine statistical differences between treatment planners before and after implementing AC and AM.Results: Implementing AC and AM resulted in an overall 13.7 ± 1.7-min reduction in planning time. Inter-user variability in organ and target dose metrics was reduced for most structures. Most notably, statistically significant differences in rectum D2cc and target V95 were observed between planners before, but not after, AC and AM implementation.Conclusions: AC and AM significantly improve workflow efficiency while maintaining plan quality. Reductions in inter-user variability and standardization of workflows may facilitate user training and robust outcome assessment.

摘要翻译： 

背景/目的：高剂量率近距离放射治疗（HDR-BT）是一种资源密集型治疗方式。其工作流程效率及操作者间的差异性仍是普遍存在的问题。为提高工作流程效率并减少操作者间的差异性，我们将基于人工智能（AI）的器官轮廓勾画（AC）与施源器建模（AM）工具引入临床工作流程。本文旨在展示这些工具对阴道残端HDR-BT病例在工作流程效率、操作者间差异性及计划质量方面的影响结果。 方法：本研究共纳入260次治疗分次，其中半数在实施AC与AM前完成。由五位不同的医学物理师执行治疗计划设计。通过系统内置时间戳记录每次分次的轮廓勾画、剂量计划及总计划设计时间。记录的剂量学数据包括膀胱与直肠最高2cc体积所受剂量（D2cc），以及90%-200%等剂量线覆盖靶区体积百分比（V90-200）。采用方差分析及事后Tukey检验，评估实施AC与AM前后不同计划设计者之间的统计学差异。 结果：实施AC与AM后，计划设计时间总体减少13.7±1.7分钟。多数结构的器官与靶区剂量指标在操作者间的差异性显著降低。最显著的是，在实施AC与AM前，不同计划设计者间的直肠D2cc与靶区V95存在统计学显著差异，而实施后该差异消失。 结论：AC与AM在保持计划质量的同时，显著提升了工作流程效率。操作者间差异性的降低与工作流程的标准化，有助于用户培训的开展与治疗结果的稳健评估。

原文链接：

Workflow Efficiency in Vaginal Cuff High Dose Rate Brachytherapy Using Artificial Intelligence-Based Organ Segmentation and Multi-Channel Cylinder Modeling