Abstract
Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers. While deep learning models automate this process, their rigid reliance on expert-annotated data requires costly retraining whenever clinical guidelines update. To overcome this limitation, we introduce OncoAgent, a novel guideline-aware AI agent framework that seamlessly converts textual clinical guidelines into three-dimensional target contours in a training-free manner. Evaluated on esophageal cancer cases, the agent achieves a zero-shot Dice similarity coefficient of 0.842 for the CTV and 0.880 for the planning target volume, demonstrating performance highly comparable to a fully supervised nnU-Net baseline. Notably, in a blinded clinical evaluation, physicians strongly preferred OncoAgent over the supervised baseline, rating it higher in guideline compliance, modification effort, and clinical acceptability. Furthermore, the framework generalizes zero-shot to alternative esophageal guidelines and other anatomical sites (e.g., prostate) without any retraining. Beyond mere volumetric overlap, our agent-based paradigm offers near-instantaneous adaptability to alternative guidelines, providing a scalable and transparent pathway toward interpretability in radiotherapy treatment planning.
Abstract (translated)
在放射治疗中，划定临床靶区体积（CTV）涉及根据肿瘤位置和解剖障碍来确定复杂的边界。尽管深度学习模型能够自动化这一过程，但它们对专家标注数据的严格依赖意味着每次临床指南更新时都需要进行昂贵的数据再训练。为了解决这个问题，我们引入了OncoAgent，这是一种新型的基于指南的人工智能代理框架，它可以在无需培训的情况下将文本形式的临床指南转换成三维目标轮廓。在针对食管癌病例的评估中，该代理实现了CTV和计划靶区体积（PTV）上的零样本Dice相似系数分别为0.842和0.880，显示出了与全监督nnU-Net基线相比高度相当的表现。值得注意的是，在盲法临床评价中，医生们更倾向于选择OncoAgent而不是监督学习的基准模型，他们认为该代理在遵守指南、修改努力以及临床接受度方面优于其他方法。此外，该框架能够在没有再训练的情况下零样本泛化到不同的食管指南以及其他解剖部位（如前列腺）上。除了体积重叠之外，我们的基于代理的方法还提供了近乎即时地适应不同指南的能力，为放射治疗计划的可解释性提供了一条可扩展且透明的道路。
URL
https://arxiv.org/abs/2603.09448
