整合多癌种影像与报告，助力医学AI研究
PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT and Radiology Report Dataset for Medical Imaging Research
- 收集490例患者多癌种全身PET/CT与报告
- 含配对影像、脱敏文本报告及结构化临床数据
- 适合医学影像、AI、多模态学习研究者使用
公开的大型医学影像数据集对人工智能模型开发与回顾性临床研究至关重要。然而，涵盖多种癌症类型的多功能与解剖影像结合详细临床报告的数据集仍十分稀缺。本文提出PETWB-REP，一个经过筛选的数据集，包含490名被诊断为多种恶性肿瘤患者的全身18F-氟代葡萄糖（FDG）正电子发射断层扫描/计算机断层扫描（PET/CT）影像及对应的放射科报告。数据集主要涵盖肺癌、肝癌、乳腺癌、前列腺癌和卵巢癌等常见癌症类型。该数据集包含配对的PET与CT图像、去标识化的文本报告以及结构化的临床元数据，旨在支持医学影像、放射组学、人工智能及多模态学习等领域的研究。
原文摘要 · Abstract (English)
Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence models and conducting retrospective clinical research. However, datasets that combine functional and anatomical imaging with detailed clinical reports across multiple cancer types remain scarce. Here, we present PETWB-REP, a curated dataset comprising whole-body 18F-Fluorodeoxyglucose (FDG) Positron Emission Tomography/Computed Tomography (PET/CT) scans and corresponding radiology reports from 490 patients diagnosed with various malignancies. The dataset primarily includes common cancers such as lung cancer, liver cancer, breast cancer, prostate cancer, and ovarian cancer. This dataset includes paired PET and CT images, de-identified textual reports, and structured clinical metadata. It is designed to support research in medical imaging, radiomics, artificial intelligence, and multi-modal learning.
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