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基于多中心深度学习的CTV和PTV在子宫恶性瘤CT成像中的自动划分

Bichun Xu¹, Jun Liu², Mingming Fang²

¹Department of Radiotherapy, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

|October 21, 2025

概括

本研究引入了一种深度学习模型,用于使用CT扫描自动概述临床目标体积 (CTV) 和使用CT扫描在子宫癌中规划目标体积 (PTV). 该模型在不同癌症类型和机构中显示出高准确性和通用性,简化了放射治疗工作流程.

科学领域:

医疗成像医学成像
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:

临床目标体积 (CTV) 和计划目标体积 (PTV) 的准确划分对于有效的子宫癌放射治疗至关重要.
手动轮是耗时的,主观的,目前的自动化方法缺乏广泛的验证.
深度学习为准确和高效的目标体积划分提供了一个潜在的解决方案.

研究的目的:

开发和验证一个深度学习模型,用于使用CT成像在多种子宫恶性瘤中自动CTV和PTV划分.
评估模型在不同癌症类型和机构中的通用性.
评估该模型在提高放射治疗规划效率和一致性方面的潜力.

主要方法:

一个深度学习模型被训练并测试在602个增强对比度的CT扫描 (宫癌和子宫内膜癌) 上.
数据集包括内部 (302个案例) 和外部 (300个案例) 队列,用于全面验证.
使用子相似系数 (DSC),95%的豪斯多夫距离 (HD95) 和平均表面距离 (ASD) 来评估性能.

主要成果:

该模型实现了高细分精度,在内部测试集中,PTV的平均DSC为83.42%,CTV的平均DSC为81.23%.
在外部测试队列 (不同类型的癌症和机构) 中观察到强的表现,表明强烈的概括性.
大约90%的模型生成的轮需要专家瘤学家进行最小的修改或不需要修改.

结论:

开发了一个多中心验证的深度学习框架,用于在子宫恶性瘤中自动CTV和PTV划分.
该模型展示了可扩展性和通用性,为减少工作量和提高辐射瘤学的一致性提供了一个有希望的工具.
这种自动化方法有可能在放射治疗规划中显著简化临床工作流程.

关键词:

自动划分临床目标体积.多机构验证的验证.子宫恶性瘤放射治疗放射治疗在 nnU-Net 中使用.

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