PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT and Radiology Report Dataset for Medical Imaging Research
This dataset addresses a gap for researchers in medical imaging, radiomics, and AI by providing a resource for developing and validating models, but it is incremental as it primarily offers new data rather than novel methods.
The authors tackled the scarcity of large-scale medical imaging datasets combining functional and anatomical imaging with clinical reports across multiple cancer types by presenting PETWB-REP, a curated dataset comprising whole-body FDG PET/CT scans and radiology reports from 490 patients with various malignancies, including common cancers like lung, liver, breast, prostate, and ovarian cancer.
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.