Radiotherapy treatment planning often relies on time-consuming, trial-and-error adjustments that heavily depend on the expertise of specialists, while existing deep learning methods face limitations in generalization, prediction accuracy, and clinical applicability. To tackle these challenges, we propose ADDiff-Dose, an Anatomical-Dose Dual Constraints Conditional Diffusion Model for end-to-end multi-tumor dose prediction. The model employs LightweightVAE3D to compress high-dimensional CT data and integrates multimodal inputs, including target and organ-at-risk (OAR) masks and beam parameters, within a progressive noise addition and denoising framework. It incorporates conditional features via a multi-head attention mechanism and utilizes a composite loss function combining MSE, conditional terms, and KL divergence to ensure both dosimetric accuracy and compliance with clinical constraints. Evaluation on a large-scale public dataset (2,877 cases) and three external institutional cohorts (450 cases in total) demonstrates that ADDiff-Dose significantly outperforms traditional baselines, achieving an MAE of 0.101-0.154 (compared to 0.316 for UNet and 0.169 for GAN models), a DICE coefficient of 0.927 (a 6.8% improvement), and limiting spinal cord maximum dose error to within 0.1 Gy. The average plan generation time per case is reduced to 22 seconds. Ablation studies confirm that the structural encoder enhances compliance with clinical dose constraints by 28.5%. To our knowledge, this is the first study to introduce a conditional diffusion model framework for radiotherapy dose prediction, offering a generalizable and efficient solution for automated treatment planning across diverse tumor sites, with the potential to substantially reduce planning time and improve clinical workflow efficiency.

翻译：放射治疗计划制定通常依赖于耗时且依赖专家经验的试错调整过程，而现有深度学习方法在泛化能力、预测精度和临床适用性方面存在局限。为应对这些挑战，我们提出ADDiff-Dose——一种基于解剖结构与剂量双重约束的条件扩散模型，用于端到端多肿瘤剂量预测。该模型采用LightweightVAE3D压缩高维CT数据，在渐进式噪声添加与去噪框架中整合多模态输入（包括靶区和危及器官掩膜及射束参数）。通过多头注意力机制融入条件特征，并采用结合MSE、条件项与KL散度的复合损失函数，确保剂量学精度与临床约束合规性。在大规模公共数据集（2,877例）和三个外部机构队列（共450例）上的评估表明，ADDiff-Dose显著优于传统基线模型：MAE达到0.101-0.154（UNet为0.316，GAN模型为0.169），DICE系数达0.927（提升6.8%），脊髓最大剂量误差控制在0.1 Gy以内。单病例平均计划生成时间缩短至22秒。消融实验证实结构编码器将临床剂量约束合规性提升28.5%。据我们所知，这是首个将条件扩散模型框架引入放疗剂量预测的研究，为跨肿瘤部位的自动化治疗计划制定提供了通用高效的解决方案，有望显著缩短计划时间并提升临床工作流程效率。