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.
Beyond the Pixel-Wise Loss for Topology-Aware Delineation
Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院计算机视觉实验室）；ARTORG Center for Biomedical Engineering Research, University of Bern（伯尔尼大学ARTORG生物医学工程研究中心）
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
National Institutes of Health (NIH)（美国国立卫生研究院）；The NIAID Integrated Research Facility（NIAID综合研究设施）；University of Pennsylvania（宾夕法尼亚大学）
在临床环境中，对从计算机断层摄影（CT）扫描中准确且快速地提取肺体积的需求仍然很大，因为由于肺的广泛解剖变化和病理的存在，可用的方法不能提供通用的解决方案。手动注释是目前的黄金标准，耗时且经常受到人为偏见的影响。另一方面，当前最先进的全自动肺分割方法由于不能有效地结合用于处理错误分类和实践的人类输入而未能进入临床实践。本文提出了一种用于CT图像的肺注释工具，该工具是交互式的、高效的和鲁棒的。建议的注释工具产生一个“尽可能准确”的模糊连通性图像分割的基础上的初始注释，然后由有效的手动固定的初始提取，如果认为有必要的从业者。为了给用户提供最大的灵活性，我们的注释工具支持三种主要的操作系统（Windows、Linux和Mac OS X）。将我们的免费软件与市售肺分割工具进行比较的定量结果显示，我们的软件具有更高的一致性和精度，具有相当大的潜力来提高常规临床任务的性能。
Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT
The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Huadong Hospital Affiliated to Fudan University（复旦大学附属华东医院）；Lihuili Hospital, Ningbo Medical Center（宁波市医疗中心李惠利医院）；The First Hospital of Lanzhou University（兰州大学第一医院）；Haining People's Hospital（海宁市人民医院）；Ping An Technology（平安科技）
Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
The Netherlands Cancer Institute – Antoni van Leeuwenhoek（荷兰癌症研究所 – 安东尼·范·列文虎克）；National Center for Tumor Diseases (NCT), NCT/UCC Dresden（国家肿瘤疾病中心（NCT），NCT/UCC 德累斯顿）；DKFZ（德国癌症研究中心）；Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学医学院及卡尔·古斯塔夫·卡鲁斯大学医院）；The Centre for Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology（德累斯顿工业大学触觉互联网与人机协同中心）；University of Twente（特文特大学）
Quality assurance of organs-at-risk delineation in radiotherapy
School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家癌症临床研究中心/肿瘤医院深圳医院）；Faculty of Health Sciences, University of Macau（澳门大学健康科学学院）；Department of Radiation Oncology, Mayo Clinic Arizona（梅奥诊所亚利桑那分院放射肿瘤科）
Improving Deep Learning-Based Target Volume Auto-Delineation for Adaptive MR-Guided Radiotherapy in Head and Neck Cancer: Impact of a Volume-Aware Dice Loss
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生委员会）；United Lincolnshire Hospitals NHS Trust（联合林肯郡医院NHS信托）；Jersey General Hospital（泽西总医院）；Eastbourne District General Hospital（伊斯特本地区综合医院）
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
PAII Inc.（PAII公司）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Huadong Hospital Affiliated to Fudan University（复旦大学附属华东医院）；First Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第一附属医院）；People's Hospital of Shanxi Province（山西省人民医院）；Nanfang Hospital, Southern Medical University（南方医科大学南方医院）；The First Hospital of Lanzhou University（兰州大学第一医院）；Chang Gung University（长庚大学）
Deep learning-based auto-segmentation of paraganglioma for growth monitoring
Centrum Wiskunde & Informatica (CWI)（数学和计算机科学中心）；Leiden University Medical Center (LUMC)（莱顿大学医学中心）；University Medical Centre Utrecht (UMCU)（乌得勒支大学医学中心）
Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人机交互与机器人学研究所）；Institute for AI in Medicine, University Hospital Essen（埃森大学医院医学人工智能研究所）；Steinbuch Center for Computing, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院（KIT）施泰因布赫计算中心）；Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ）放射肿瘤学医学物理科）；Heidelberg Institute of Radiation Oncology (HIRO)（海德堡放射肿瘤学研究所（HIRO））；National Center for Radiation Research in Oncology (NCRO)（国家肿瘤放射研究中心（NCRO））；Helmholtz Information and Data Science School for Health (HIDSS4Health)（亥姆霍兹健康信息与数据科学学院（HIDSS4Health））；Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen（埃森大学医院诊断与介入放射学及神经放射学系）
