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提高前列腺癌适应性放射治疗的细分精度,使用患者特定的网络

Joonil Hwang¹, Byung-Hee Kang², Younghee Park²

¹Department of Nuclear and Quantum Engineering, MIRLAB, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

|September 19, 2025

概括

一个新的患者特定网络 (PSN) 通过在日常扫描上准确地细分瘤来改善前列腺癌的适应性放射治疗. 这种方法提高了处理精度和工作流程效率,减少了手动校正.

科学领域:

辐射瘤学辐射瘤学
医疗成像医学成像
人工智能在医学中的应用

背景情况:

适应性放射治疗 (ART) 通过适应日常解剖变化来改善前列腺癌治疗.
临床ART实施受到准确和高效的自动细分需求的阻碍,往往需要手动纠正.
当前的方法在治疗过程中与解剖学变化的动态性质作斗争.

研究的目的:

开发和评估一个针对患者的网络 (PSN),用于精确的临床目标体积 (CTV) 细分,用于前列腺ART的形光束计算断层扫描 (CBCT).
评估PSN框架的性能,包括自适应和顺序微调策略,与现有方法对比.
确定PSN方法是否可以提高临床实践中的细分精度和简化工作流效率.

主要方法:

一项对26名前列腺癌患者 (119分) 的回顾性研究,使用CBCT指导的在线ART.
使用Swin UNETR架构实施两阶段PSN框架 (预培训和患者特定微调).
将PSN性能 (DSC,HD,MSD) 与可变形注册和通用深度学习模型进行比较.

主要成果:

与现有方法相比,PSN框架显著提高了细分精度.
PSNadaptive在连续改进的过程中,在第五个小部分实现了高性能指标 (DSC: 0.978,HD: 1.681 mm,MSD: 0.510 mm).
视觉评估显示与医生轮有很强的一致性,特别是在复杂的解剖区域.

结论:

在前列腺ART中,PSN框架为患者特定的CTV细分提供了临床可行和准确的解决方案.
这种方法有可能显著减少手动轮编辑,从而提高工作流的效率.
通过PSN提高细分精度,可以为前列腺癌患者提供更精确的适应性放射治疗.

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