https:\/\/autopet.grand-challenge.org\/<\/a>.\u00a0\u00a0<\/p>Data<\/strong>: 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). 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<\/p>","collection_acknowledgements":"
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 to
- 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 to
- Clemens Cyran, MD<\/strong> from the Department of Radiology<\/li>
- Michael Ingrisch<\/strong> from the Department of Radiology<\/li><\/ul><\/li><\/ul>","collection_funding":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41513","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_collection"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41513\/revisions"}],"predecessor-version":[{"id":47829,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/41513\/revisions\/47829"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/6707"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=41513"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}