深度学习用于头部和部辐射剂量预测:系统性审查和元分析
Maryam Zamanian1, Mohammad Ali Kavehpoor1, Amir Mohammad Soltaninejad1
1Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Scientific reports
|December 8, 2025
概括
卷积神经网络 (CNN) 在预测头癌的辐射剂量方面表现有前途. 先进的CNN在预测目标体积方面表现出色,而经典的CNN则提供一致的脊髓剂量预测.
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:
- 精确的放射治疗剂量分配对于头癌治疗至关重要.
- 解剖学变异性和关键器官的接近性在剂量预测方面带来了挑战.
- 卷积神经网络 (CNN) 正在探索,以提高预测的准确性.
研究的目的:
- 进行CNN的元分析,以预测头部和部放射治疗中的剂量分布.
- 评估不同CNN架构的预测效果,用于规划目标体积 (PTV) 和脊髓剂量.
- 确定影响CNN在放射治疗剂量预测中的表现的因素.
主要方法:
- 在MEDLINE,Embase,Scopus和Scholar数据库中的系统文献搜索.
- 对PTV D95和脊髓Dmax的预测错误 (MAE) 的元分析.
- 使用PROBAST工具对方法质量的评估.
- 基于放射治疗技术,网络设计和癌症类型的亚组分析.
主要成果:
- 先进的CNN实现了PTV D95预测的更高准确度 (MAE:0.95),但具有显著的异质性.
- 经典的CNN提供了更一致的脊髓Dmax预测 (MAE:0.95),异质性较低.
- 托莫疗法,特定网络设计和NPC案例显示预测性能存在差异.
结论:
- CNN显示了准确的放射治疗剂量预测的潜力,建议对特定任务进行定制.
- 先进的CNN在PTV预测方面优越,而经典的CNN在脊髓预测方面更加一致.
- 标准化报告对于放射治疗中CNN模型的可复制性和临床翻译至关重要.
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