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基于深度学习的剂量图预测用于高剂量率支臂治疗

Zhen Li¹, Zhenyu Yang², Jiayu Lu³

¹Shanghai Sixth People's Hospital, Shanghai, People's Republic of China.

Physics in medicine and biology

|August 17, 2023

概括

一个新的深度学习模型,挤压和兴奋注意力网 (SE_AN),准确地预测了手臂治疗剂量分布. 这种人工智能工具有望提高临床实践中的治疗计划效率和标准化.

科学领域:

医学物理医学物理
辐射疗法辐射疗法
人工智能在医学中的应用

背景情况:

在临床工作流程中,支臂疗法治疗计划是耗时且具有挑战性的.
现有的深度学习剂量预测模型主要用于外部束辐射疗法 (EBRT),并未优化用于支臂疗法.
缺乏专门用于支臂治疗剂量预测的成熟的深度学习模型.

研究的目的:

开发和评估一种新的挤压和兴奋注意力网 (SE_AN),用于准确的3D支臂治疗剂量分布预测.
为了强调应用器几何学的重要性,在布拉基疗法的剂量预测.

主要方法:

一个新的 SE 模块被集成到 Cascaded UNet 架构中,以增强功能重新校准,创建 SE_AN 模型.
在250个临床支臂治疗病例中训练并验证了SE_AN模型,其中包括各种应用器类型 (阴道,体和卵状,多通道,自由针).
通过将预测的剂量分布与临床批准的计划进行比较,使用剂量-体积组图 (DVH) 度量 (D2cc,D90%) 的平均绝对误差 (MAE) 来评估模型性能.

主要成果:

对于关键结构,SE_AN模型实现了较低的MAE:HRCTV D90的0.37 ± 0.25 Gy,膀 D2cc的0.23 ± 0.14 Gy,直肠 D2cc的0.28 ± 0.20 Gy.
在预测DVH指标方面,SE_AN在预测DVH指标方面表现出与经典UNet和级联UNet模型相比的或更高的准确性.
该模型的预测与经验丰富的剂量计师创建的计划中的剂量分布非常相匹配.

结论:

已经成功开发了一种专门的深度学习方法 (SE_AN) 用于3D支臂治疗剂量预测.
该SE_AN模型提供与临床标准可比的准确剂量预测.
这种技术有可能提高支臂疗法治疗计划的标准化和质量控制.

关键词:

支臂疗法 (brachytherapy) 是一种治疗方法.宫癌:子宫癌是一种癌症.深度学习是一种深度学习.剂量预测剂量预测

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