Summary<\/h1>Purpose: To provide an annotated data set of oncologic PET\/CT studies for the development and training of machine learning methods and to help address the limited availability of publicly available high-quality training data for PET\/CT image analysis projects.\u00a0 This data can also be used for machine learning challenges, which is exemplified in the autoPET MICCAI 2022 competition: https:\/\/autopet.grand-challenge.org\/<\/a>.\u00a0\u00a0<\/p>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 T\u00fcbingen 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.<\/p>
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).<\/p>
We provide the anonymized original DICOM files of all studies as well as the DICOM segmentation masks. Primary diagnosis, age and sex are provided as non-imaging information (csv). <\/span>In addition, we provide links to code for you to make a preprocessed version of the data with resampled and aligned PET, CT, and masks as a NIfTI file and in the hdf5 format ready to use in machine learning projects.\u00a0Acknowledgements<\/span><\/h3>We would like to acknowledge the individuals and institutions that have provided data for this collection:<\/p>
- University Hospital T\u00fcbingen, T\u00fcbingen, Germany - Special thanks
- Christian La Foug\u00e8re, MD <\/strong>from the Department of Nuclear Medicine\u00a0<\/li>
- Tobias Hepp, MD<\/strong> from the Department of Radiology<\/li>
- Konstantin Nikolaou, MD<\/strong> from the Department of Radiology<\/li>
- Christina Pfannenberg, MD<\/strong> from the Department of Radiology\u00a0<\/li><\/ul><\/li>
- University Hospital of the LMU (Munich), Germany \u2013 Special thanks
- Clemens Cyran, MD<\/strong> from the Department of Radiology<\/li>
- Michael Ingrisch<\/strong> from the Department of Radiology<\/li><\/ul><\/li><\/ul><\/div>
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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 T\u00fcbingen 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.<\/p>
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).<\/p>
We provide the anonymized original DICOM files of all studies as well as the DICOM segmentation masks. Primary diagnosis, age and sex are provided as non-imaging information (csv). <\/span>In addition, we provide links to code for you to make a preprocessed version of the data with resampled and aligned PET, CT, and masks as a NIfTI file and in the hdf5 format ready to use in machine learning projects.\u00a0 We would like to acknowledge the individuals and institutions that have provided data for this collection:<\/p>Acknowledgements<\/span><\/h3>
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