This dataset represents the PANORAMA: Public Training and Development Dataset. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the Medical Segmentation Decathlon dataset and 80 cases from National Institutes of Health. For all updates/fixes regarding this dataset, please join the challenge and check out our dedicated forum post on this topic. The corresponding labels of the PANORAMA dataset can be found here. The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT. Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (Reinke et al., 2021). This PANORAMA dataset contains: batch 1 out of 4

Natália Alves and Megan Schuurmans contributed equally to this work.