在乳腺临床试验中,用于计划质量的自动轮和统计过程控制
Hana Baroudi1,2, Callistus I Huy Minh Nguyen2, Sean Maroongroge3
1The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
Physics and imaging in radiation oncology
|September 15, 2023
概括
一个自动化的轮模型和统计过程控制有效地评估了临床试验中的乳腺放射治疗计划的一致性,提高了效率和质量审查.
科学领域:
- 医学物理 医学物理
- 放射治疗瘤学 放射治疗瘤学
- 人工智能在医学中的应用
背景情况:
- 由于缺少目标轮,对乳腺放射治疗计划质量的自动审查具有挑战性.
- 现有的方法耗时,缺乏对规划一致性的客观评估.
研究的目的:
- 开发和验证一个与统计过程控制 (SPC) 结合的自动化轮模型.
- 在乳腺放射治疗临床试验的回顾性数据中评估计划的一致性.
主要方法:
- 在CT图像上训练和测试了一种深度学习自动轮模型 (nnUNet).
- 该模型应用于127名临床试验患者;SPC评估了剂量的一致性.
- 医生审查了SPC标记的计划,以寻找潜在的不一致性.
主要成果:
- 自动轮实现了关键结构的Dice相似系数>0.7,临床可接受度为95%.
- 淋巴结和乳腺之间,以及VMAT和3D-CRT技术之间,剂量变化有所不同.
- 五个计划 (5%) 被标记,其中一个需要编辑,另一个显示可接受的变化或糟糕的自动轮.
结论:
- 在SPC框架内的自动化轮模型适用于评估乳腺放射治疗计划的一致性.
- 这种方法提高了临床试验质量保证的效率和客观性.
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