Data: The anonymized publication of data was approved by the local ethics committee and data protection officer. 500 consecutive whole body FDG-PET/CT data sets of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) examined in 2014 and 2018 at the University Hospital Tuebingen 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 (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, prior examinations, and follow-up data were manually segmented on PET images slice-wise 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 segmentation masks as Nifti files. In addition, we aim to provide a preprocessed version of the data with resampled and aligned PET, CT, and masks as a HDF5 file ready to use in machine learning projects. Primary diagnosis, age and sex are provided as non-imaging information.