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.

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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
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article
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article

PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports

Liling Peng, Wenbo Zhang, Shuqi Wang, Yichi Zhang et al.
Scientific Data
Radiomics and Machine Learning in Medical Imaging
article

PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports

Liling Peng, Wenbo Zhang, Shuqi Wang, Yichi Zhang, Xin D. Gao, Lanlan Li, Long Xue, Gang Feng, Sisi Peng
article en

Abstract

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.

Scientific Data
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)
Openalex Percentile: Top 24%
Radiomics and Machine Learning in Medical Imaging
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PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports — Liling Peng, Wenbo Zhang, et al. · Scientific Data (2026) | TGRS Research Map | TGRS