PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports
Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence (AI) models and conducting retrospective clinical research. However, multimodal datasets that integrate functional and anatomical imaging with high-quality radiology reports across diverse malignancies remain scarce. Here, we present PETWB-REP, a curated dataset comprising whole-body <sup>18</sup>F-Fluorodeoxyglucose (FDG) Positron Emission Tomography/Computed Tomography (PET/CT) scans and corresponding radiology reports from 490 patients. The cohort encompasses a broad spectrum of malignancies, including but not limited to lung, liver, breast, prostate, and ovarian cancers. Distinct from existing resources, PETWB-REP is organized following the Brain Imaging Data Structure (BIDS) standard, providing both raw data (with 3D de-facing for privacy) and processed derivatives (SUV-converted and registered). Each case includes bilingual (Chinese and English) de-identified textual reports and structured clinical metadata. This dataset is uniquely positioned to support multi-center validation and cross-disciplinary research in medical imaging, radiomics, automated report generation, and multimodal representation learning.
Authors
- Liling Peng
- Wenbo Zhang (ORCID: https://orcid.org/0009-0001-8602-2467)
- Shuqi Wang (ORCID: https://orcid.org/0000-0002-4460-7221)
- Yichi Zhang (ORCID: https://orcid.org/0000-0002-2280-7599)
- Xin D. Gao (ORCID: https://orcid.org/0000-0002-7108-3574)
- Lanlan Li (ORCID: https://orcid.org/0000-0002-7068-698X)
- Long Xue
- Gang Feng
- Sisi Peng
Institutions
- Fudan University (CN)
- Shanghai Institute for Science of Science (CN)
- Shanghai Medical Information Center (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Shanghai Institute of Hematology (CN)
- Huashan Hospital (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- Scientific Data
- Published
- 2026-03-13
- DOI
- https://doi.org/10.1038/s41597-026-07058-w
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00