Radiation therapy (RT) is one of the most effective treatments for cancer, and its success relies on the accurate delineation of targets. However, target delineation is a comprehensive medical decision that currently relies purely on manual processes by human experts. Manual delineation is time-consuming, laborious, and subject to interobserver variations. Although the advancements in artificial intelligence (AI) techniques have significantly enhanced the auto-contouring of normal tissues, accurate delineation of RT target volumes remains a challenge. In this study, we propose a visual language model-based RT target volume auto-delineation network termed Radformer. The Radformer utilizes a hierarichal vision transformer as the backbone and incorporates large language models to extract text-rich features from clinical data. We introduce a visual language attention module (VLAM) for integrating visual and linguistic features for language-aware visual encoding (LAVE). The Radformer has been evaluated on a dataset comprising 2985 patients with head-and-neck cancer who underwent RT. Metrics, including the Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to evaluate the performance of the model quantitatively. Our results demonstrate that the Radformer has superior segmentation performance compared to other state-of-the-art models, validating its potential for adoption in RT practice.
Deep Learning-Based Automatic Delineation of Liver Domes in kV Triggered Images for Online Breath-hold Reproducibility Verification of Liver Stereotactic Body Radiation Therapy
Cleveland Clinic Foundation（克利夫兰医学中心）；Case Western Reserve University（凯斯西储大学）
Virtual Speech Therapist: A Clinician-in-the-Loop AI Agent for Speech Therapy Planning
The Kashmir Hub for Artficial Intelligence（喀布尔人工智能中心）；Microsoft / Vocametrix（微软 / Vocametrix）；IAI, TCG CREST（IAI，TCG CREST）；Université de Lorraine, CNRS, Inria, LORIA（洛林大学，CNRS，Inria，LORIA）；Laboratoire Praxiling, UMR5267, CNRS et Université Paul-Valéry Montpellier 3（Praxiling实验室，UMR5267，CNRS及蒙彼利埃Paul-Valéry大学）；Speechcare iStutter, Portuguese Catholic University（Speechcare iStutter，葡萄牙天主教大学）；CHI – Chair of Health Informatics, TUM University Hospital（健康信息学系，TUM大学医院）；GLAM – Group on Language, Audio, & Music, Imperial College London（语言、音频与音乐小组，伦敦帝国理工学院）
TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots
Department of Computer Science, Stanford University, Stanford, CA, USA（计算机科学系，斯坦福大学，斯坦福，加州，美国）；Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学，斯坦福，加州，美国）；Department of Biomedical Data Science, Stanford University, Stanford, CA, USA（生物医学数据科学系，斯坦福大学，斯坦福，加州，美国）；University of California, Berkeley, Berkeley, CA, USA（加州大学伯克利分校，伯克利，加州，美国）；The University of Hong Kong, Hong Kong（香港大学，香港）
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Moscow Gamma-Knife Center（莫斯科伽马刀中心）；Burdenko Neurosurgery Institute（布尔坚科神经外科研究所）
Divergent Clinical Equivalence Findings from DVH and NTCP Metrics for Alternative OAR Delineations with Increasing Setup Variability in Head-and-Neck Radiotherapy
Data harvesting vs data farming: A study of the importance of variation vs sample size in deep learning-based auto-segmentation for breast cancer patients
Patient-Specific Deep Reinforcement Learning for Automatic Replanning in Head-and-Neck Cancer Proton Therapy
Department of Radiation and Cellular Oncology University of Chicago（辐射与细胞肿瘤学系，芝加哥大学）；Division of Environmental and Occupational Health Sciences University of Illinois at Chicago（环境与职业卫生科学系，伊利诺伊大学芝加哥分校）；Department of Radiation Oncology and Winship Cancer Institute Emory University（放射肿瘤学系和Winship癌症研究所，埃默里大学）
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）
