评估使用深度学习的前列腺IMRT患者的剂量分配:损失功能的对模型性能影响
Arezoo Kazemzadeh1, Reza Rasti2, Alireza Amouheidari3
1Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Physical and engineering sciences in medicine
|February 2, 2026
概括
选择正确的损失函数可以显著改善前列腺强度调节放射治疗剂量预测的深度学习模型. 结合领域知识,如瞬间损失,提高了个性化辐射规划的准确性.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 机器学习 机器学习
背景情况:
- 深度学习 (DL) 模型越来越多地用于放射治疗剂量预测.
- 优化这些模型需要仔细选择损失函数.
- 前列腺癌强度调节放射治疗 (IMRT) 的规划提出了独特的剂量测量挑战.
研究的目的:
- 评估不同损失函数对前列腺IMRT剂量预测DL模型性能的影响.
- 为了比较标准的损失函数 (MAE,MSE) 与那些包含域特定知识 (DVH损失,时刻损失) 的函数.
- 评估IMRT中基于DL的剂量预测的临床适用性.
主要方法:
- 110例前列腺癌患者病例的回顾性分析.
- 训练DL模型具有平均绝对误差 (MAE),平均平方误差 (MSE),MAE+剂量-体积直方形 (DVH) 损失,以及MAE+时刻损失.
- 使用计划目标体积 (PTV) 和风险器官 (OAR) 剂量计量指标进行性能评估.
- 使用单向差异分析 (ANOVA) 的统计比较.
主要成果:
- 用MAE加上时刻损失训练的DL模型在减少OAR和PTV的剂量偏差方面表现出卓越的性能.
- 平均绝对误差 (MAE) ±标准偏差 (SD) 对于MAE+动量损失,MAE+DVH损失,MSE和MAE分别为 (1.76 ± 0.5) Gy, (1.78 ± 0.5) Gy, (1.93 ± 0.6) Gy和 (2.02 ± 0.4) Gy.
- 与地面真相计划相比,所有预测的剂量分配都实现了临床上可接受的准确性.
结论:
- 损失功能选择对于优化基于DL的前列腺IMRT剂量预测至关重要.
- 将特定领域的知识集成到损失函数中,特别是时刻损失,可以显著提高模型性能.
- 这些发现支持DL模型的实际应用,用于精确和个性化的放射治疗计划.
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