用用户偏好生成放疗计划，摆脱机构风格依赖。
Demo: Generative AI helps Radiotherapy Planning with User Preference
- 基于用户自定义偏好生成三维剂量分布，不依赖参考计划。
- 在部分场景下优于Varian RapidPlan，计划质量与适应性更优。
- 适合希望个性化定制放疗方案的临床医生使用。
放疗计划制定过程复杂，不同机构和规划师间差异显著。现有深度学习方法在3D剂量预测中通常以参考计划作为训练标签，可能无意中使模型偏向特定规划风格或机构偏好。本研究提出一种新型生成模型，仅根据用户定义的偏好风味预测3D剂量分布。这些可定制偏好使规划师能优先考虑器官危及组织（OARs）与靶区（PTVs）之间的特定权衡，提供更高灵活性与个性化。该方法设计用于无缝集成至临床治疗计划系统，可高效辅助生成高质量计划。对比评估显示，在某些场景下，该方法在适应性与计划质量上超越Varian RapidPlan模型。
原文摘要 · Abstract (English)
Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing deep learning approaches for 3D dose prediction rely on reference plans as ground truth during training, which can inadvertently bias models toward specific planning styles or institutional preferences. In this study, we introduce a novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors. These customizable preferences enable planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs), offering greater flexibility and personalization. Designed for seamless integration with clinical treatment planning systems, our approach assists users in generating high-quality plans efficiently. Comparative evaluations demonstrate that our method can surpasses the Varian RapidPlan model in both adaptability and plan quality in some scenarios.
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