Summary
Data: The anonymized publication of data was approved by the local ethics committee and data protection officer. 501 consecutive whole body FDG-PET/CT data sets of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) as well as 513 data sets without PET-positive malignant lesions (negative controls) examined between 2014 and 2018 at the University Hospital Tübingen were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner (Siemens Biograph mCT). The imaging protocol consists of a diagnostic CT scan (mainly from skull base to mid-thigh level) with intravenous contrast enhancement in most cases, except for patients with contraindications. The following CT parameters were used: reference dose of 200 mAs, tube voltage of 120 kV, iterative reconstruction with a slice thickness of 2 - 3 mm. In addition, a whole-body FDG-PET scan was acquired 60 minutes after I.V. injection of 300-350 MBq 18F-FDG. PET data were reconstructed using an ordered-subset expectation maximization (OSEM) algorithm with 21 subsets and 2 iterations and a gaussian kernel of 2 mm and a matrix size of 400 x 400.
All data sets were analyzed in a clinical setting by a radiologist and nuclear medicine physician in consensus identifying primary tumors and metastases in each data set. All FDG-avid lesions identified as malignant based on patient history and prior examinations were manually segmented on PET images in a slice-per-slice manner by a single reader using dedicated software (NORA imaging platform, University of Freiburg, Germany).
We provide the anonymized original DICOM files of all studies as well as the DICOM segmentation masks. In addition, we aim to provide a preprocessed version of the data with resampled and aligned PET, CT, and masks as a NIfTI file ready to use in machine learning projects. Primary diagnosis, age and sex are provided as non-imaging information.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
University Hospital Tübingen, Tübingen, Germany - Special thanks
Prof. Christian La Fougère, MD from the Department of Nuclear Medicine and
Prof. Konstantin Nikolaou from the Department of Radiology
Prof. Christina Pfannenberg from the Department of Radiology
Data Access
Data Type | Download all or Query/Filter |
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Images and Segmentations (DICOM, XX.X GB) |
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Clinical data (CSV) | |
Tumor Segmentation Masks (NIfTI, XX GB) | <link to package> |
Preprocessed aligned and resampled PET, CT and mask data compiled as HDF5 file | <link to package> |
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Detailed Description
Image Statistics | |
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Modalities | PT, CT, and SEG |
Number of Patients | 900 |
Number of Studies | 1014 |
Number of Series | 3088 |
Number of Images | 916,957 |
Images Size (GB) |
SEG are most easily reviewed as overlay using MITK viewer or 3D Slicer.
Citations & Data Usage Policy
Data Citation
Dr. Tobias Hepp (0000-0002-5241-9642)
Dr. Sergios Gatidis (0000-0002-6928-4967)
<doi coming soon>
Publication Citation
<Nature Scientific Data accompanying article coming soon>
TCIA Citation
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
Other Publications Using This Data
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Version X (Current): Updated yyyy/mm/dd
Data Type | Download all or Query/Filter |
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Images (DICOM, xx.x GB) | (Requires NBIA Data Retriever.) |
Clinical Data (CSV) | Link |
Other (format) |