10中心联合构建头颈癌多模态数据集，支持分割、诊断与预后预测。
A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction
- 整合10个中心1123例患者PET/CT影像及标注
- 涵盖肿瘤体积、淋巴结、生存时间等关键信息
- 适合做肿瘤分割、预后预测和分子分型研究
我们公开发布了一个用于头颈癌研究的多模态数据集，包含来自10个国际医疗中心的1123例经组织学确诊患者的正电子发射断层扫描/计算机断层扫描（PET/CT）研究，所有数据均经过配准，采集协议多样，反映真实临床多样性。主要原发肿瘤体积（GTVp）和受累淋巴结（GTVn）由经验丰富的放疗科医生与放射科医生根据既定指南手动勾画。数据集提供匿名化NifTi文件、专家标注的分割掩膜、全面的临床元数据以及部分患者的放疗剂量分布。元数据包括TNM分期、HPV状态、人口统计学信息、长期随访结果、生存时间、删失指示及治疗信息。为验证其价值，我们使用UNet、SegResNet及多模态预后框架等先进深度学习模型，在自动肿瘤分割、无复发生存预测和HPV状态分类三项临床任务上进行了基准测试。
原文摘要 · Abstract (English)
We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies from patients with histologically confirmed disease, acquired from 10 international medical centers. All studies contain co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity from a long-term, multi-institution retrospective collection. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following established guidelines. We provide anonymized NifTi files, expert-annotated segmentation masks, comprehensive clinical metadata, and radiotherapy dose distributions for a patient subset. The metadata include TNM staging, HPV status, demographics, long-term follow-up outcomes, survival times, censoring indicators, and treatment information. To demonstrate its utility, we benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, using state-of-the-art deep learning models like UNet, SegResNet, and multimodal prognostic frameworks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。