发表机构
University of Maryland, College Park; University of Maryland School of Medicine; University of Maryland Institute for Health Computing; University of Maryland Medical System（马里兰大学帕克分校; 马里兰大学医学院; 马里兰大学健康计算研究所; 马里兰大学医学系统）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对LI-RADS肝脏病变评估AI模型缺乏公开高质量数据集的问题，推出首个公开多期腹部CT扫描的AMPLIFAI数据集，标注LI-RADS类别并分割关键特征，助力相关透明可复现研究。
AI 中文摘要
肝细胞癌（HCC）是全球癌症相关死亡的第三大原因，早期检测可将生存率从不足20%提升至70%以上。标准化的LI-RADS标准建立了无需活检、完全基于影像的框架，可作为利用人工智能（AI）自动化HCC诊断的基础。然而，缺乏带有高质量标注的大型公开数据集限制了用于LI-RADS特征表征的AI模型的开发。我们推出AMPLIFAI数据集，这是首个公开的多期腹部CT扫描数据集，标注有LI-RADS类别，并针对LI-RADS的三个主要特征（动脉期强化、洗脱、强化包膜）进行了分割。本文遵循《数据集数据表》格式，详细介绍了该数据集的组成、整理过程及标注流程，以促进透明、可复现的研究。
英文摘要
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide, with early detection improving survival from <20% to >70%. The standardized Liver Imaging Reporting and Data System (LI-RADS) criteria provide an imaging-based diagnostic framework to evaluate liver lesions for HCC, serving as a foundation for automating HCC detection with artificial intelligence (AI). However, the lack of large, publicly available datasets with high-quality annotations has limited the development and evaluation of AI models for automated LI-RADS assessment. We introduce AMPLIFAI dataset, the first public dataset of 590 multiphase abdominal CT studies annotated with LI-RADS categories, lesion size, and voxel-level segmentations for three major LI-RADS features: arterial phase hyperenhancement, washout, and enhancing capsule. The dataset was curated and harmonized from four public datasets and augmented with expert annotations from five board-certified radiologists and one resident. Following the Datasheets for Datasets format, this paper details the dataset's composition, curation and harmonization process, and annotation workflow to support transparent, reproducible research in medical imaging AI.
Comments15 pages, 6 figures, 3 tables
