用临床剂量-体积指标直接优化3D放疗剂量预测，更贴近实际治疗需求。
Clinical DVH metrics as a loss function for 3D dose prediction in head and neck radiotherapy
- 设计可微的临床剂量指标损失函数，直接优化关键临床指标
- 提升靶区覆盖度，使所有器官保护约束均满足，误差降低68%
- 结合位掩码编码，训练效率提升83%，适合临床实用
目的：基于深度学习的三维（3D）剂量预测广泛应用于自动化放疗流程中。然而，现有模型多采用体素级回归损失，与基于剂量-体积直方图（DVH）指标的临床计划评估标准不一致。本研究旨在开发一种临床引导的损失函数，直接优化临床使用的DVH指标，同时保持头颈部（H&N）剂量预测的计算效率。方法：提出临床DVH指标损失（CDM损失），包含可微的D-指标和代理的V-指标，并结合无损位掩码区域感兴趣区（ROI）编码以提升训练效率。在174例头颈部患者数据上进行时序划分验证（137例训练，37例测试）。结果：相比MAE和基于DVH曲线的损失，CDM损失显著改善靶区覆盖并满足所有临床约束。使用标准3D U-Net，PTV评分从1.544（MAE）降至0.491（MAE + CDM），而危及器官保护性能保持相当。位掩码编码使训练时间减少83%，降低GPU内存占用。结论：直接优化临床常用的DVH指标，使3D剂量预测更契合临床治疗计划标准。所提出的CDM损失结合高效ROI位掩码编码，为头颈部剂量预测提供了实用且可扩展的框架。
原文摘要 · Abstract (English)
Purpose: Deep-learning-based three-dimensional (3D) dose prediction is widely used in automated radiotherapy workflows. However, most existing models are trained with voxel-wise regression losses, which are poorly aligned with clinical plan evaluation criteria based on dose-volume histogram (DVH) metrics. This study aims to develop a clinically guided loss formulation that directly optimizes clinically used DVH metrics while remaining computationally efficient for head and neck (H\&N) dose prediction. Methods: We propose a clinical DVH metric loss (CDM loss) that incorporates differentiable \textit{D-metrics} and surrogate \textit{V-metrics}, together with a lossless bit-mask region-of-interest (ROI) encoding to improve training efficiency. The method was evaluated on 174 H\&N patients using a temporal split (137 training, 37 testing). Results: Compared with MAE- and DVH-curve based losses, CDM loss substantially improved target coverage and satisfied all clinical constraints. Using a standard 3D U-Net, the PTV Score was reduced from 1.544 (MAE) to 0.491 (MAE + CDM), while OAR sparing remained comparable. Bit-mask encoding reduced training time by 83\% and lowered GPU memory usage. Conclusion: Directly optimizing clinically used DVH metrics enables 3D dose predictions that are better aligned with clinical treatment planning criteria than conventional voxel-wise or DVH-curve-based supervision. The proposed CDM loss, combined with efficient ROI bit-mask encoding, provides a practical and scalable framework for H\&N dose prediction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。