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深度学习对宫癌剂量预测模型的比较研究卷度调制弧形疗法

Zhe Wu^1,2, Mujun Liu¹, Ya Pang²

¹Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China.

Technology in cancer research & treatment

|April 8, 2024

概括

深度学习模型准确地预测宫癌的辐射剂量 VMAT. 3D U-Net 模型在预测 voxel 级剂量分布方面表现最好.

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:

深度学习 (DL) 在放射瘤学中越来越多地用于剂量预测.
缺乏对不同DL技术的比较研究,阻碍了临床采用.
准确的剂量预测对于优化宫癌治疗中的体积调制弧线疗法 (VMAT) 至关重要.

研究的目的:

为了比较四个最先进的深度学习模型的性能.
为了评估它们在预测子宫癌的伏塞尔水平剂量分布方面的准确性,VMAT.
为了确定这个应用程序最有效的DL模型.

主要方法:

对261名宫癌患者计划的回顾性分析.
利用3D U-Net和三个变体,在CT图像,PTV和OAR面具上进行训练.
在测试组中使用平均绝对误差 (MAE),剂量差异,剂量指数和滴滴相似系数 (DSC) 评估模型.

主要成果:

所有DL模型都显示出有希望的剂量预测准确性,在PTV范围内最大MAE为0.83% (UNETR).
风险器官 (OARs) 的最大MAE在左腿部头部 (6.95%) 观察到.
3D U-Net实现了全身的最低MAE (0.94%),优于其他模型.

结论:

深度学习模型准确地预测子宫癌的voxel级剂量分布 VMAT.
虽然模型表现出类似的性能,但3D U-Net在整体体剂量预测方面表现出卓越的结果.
这些先进的DL模型具有在宫癌VMAT中临床应用的巨大潜力.

关键词:

3D U-Net 的变体 3D剂量预测 3D剂量预测宫癌:子宫癌是一种癌症.深度学习是一种深度学习.体积调制弧线疗法 (VMAT) 是一种体积调制弧线疗法.

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