A Whole-Body PSMA-PET/CT dataset with manually annotated tumor lesions

作者：Katharina Jeblick, Balthasar Schachtner, Andreas Mittermeier, Jakob Dexl, Philipp Wesp, Thomas Küstner, Sergios Gatidis, Marcel Früh, Matthias P. Fabritius, Felix Herr, Lena Unterrainer, Konrad Klimek, Gabriel Sheikh, Guido Böning, Matthias Brendel, J Ricke, Rudolf A. Werner, Sijing Gu, Lalith Kumar Shiyam Sundar, Michael Ingrisch, Thomas Geyer, Clemens Cyran · 发表于：Scientific Data · 年份：2026 · DOI：10.1038/s41597-026-07821-z · 被引用次数：2 · 研究领域：Prostate Cancer Treatment and Research、Medical Imaging Techniques and Applications、Radiopharmaceutical Chemistry and Applications

We describe a publicly available, large, annotated dataset of 597 whole-body Positron Emission Tomography/Computed Tomography (PET/CT) studies with Prostate-Specific Membrane Antigen (PSMA)-targeting radiotracers ([18 F]PSMA and [68Ga]Ga-PSMA-11) from 378 male patients with suspected or diagnosed prostate carcinoma. Scans were acquired between 2014 and 2022 on three clinical PET/CT scanners. The imaging protocol consisted of PET and diagnostic CT acquisitions extending from the skull base to the mid-thigh. All PSMA-expressing tumor lesions were manually segmented on the PET images in 3D space using dedicated software. The dataset includes anonymized DICOM files of all PET/CT studies, corresponding DICOM segmentation masks, and a TSV file with patient age, PET/CT manufacturer and model name, PET radionuclide, and information on whether CT contrast agent was used. We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT. Together with a previously published whole-body Fluorodeoxyglucose (FDG)-PET/CT dataset, this dataset was provided in the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) registered autoPET III and IV Grand Challenges to enable the development of multi-tracer machine learning models for automated lesion segmentation in whole-body PET/CT.