6624726
doi
10.5281/zenodo.6624726
oai:zenodo.org:6624726
user-grand-challenge
user-eu
Twilt, Jasper Jonathan
Department of Medical Imaging, Radboud University Medical Center, The Netherlands
Bosma, Joeran Sander
Department of Medical Imaging, Radboud University Medical Center, The Netherlands
van Ginneken, Bram
Department of Medical Imaging, Radboud University Medical Center, The Netherlands
Yakar, Derya
Department of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, The Netherlands
Elschot, Mattijs
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Norway
Veltman, Jeroen
Department of Radiology, Ziekenhuis Groep Twente, The Netherlands
Fütterer, Jurgen
Department of Medical Imaging, Radboud University Medical Center, The Netherlands
de Rooij, Maarten
Department of Medical Imaging, Radboud University Medical Center, The Netherlands
Huisman, Henkjan
Department of Medical Imaging, Radboud University Medical Center, The Netherlands
The PI-CAI Challenge: Public Training and Development Dataset
Saha, Anindo
Department of Medical Imaging, Radboud University Medical Center, The Netherlands
doi:10.5281/zenodo.6522364
info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial 4.0 International
https://creativecommons.org/licenses/by-nc/4.0/legalcode
prostate cancer
artificial intelligence
magnetic resonance imaging
radiologists
computer-aided detection and diagnosis
<p>This dataset represents the <strong><a href="https://pi-cai.grand-challenge.org/">PI-CAI</a>: Public Training and Development Dataset</strong>. It contains 1500 anonymized prostate biparametric MRI scans from 1476 patients, acquired between 2012-2021, at three centers (Radboud University Medical Center, University Medical Center Groningen, Ziekenhuis Groep Twente) based in The Netherlands.</p>
<p>The PI-CAI challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at clinically significant prostate cancer (csPCa) detection/diagnosis in MRI, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting. The study hypothesizes that state-of-the-art AI algorithms, trained using thousands of patient exams, are non-inferior to radiologists reading bpMRI.</p>
<p>Key aspects of the PI-CAI study design have been established in conjunction with an international scientific advisory board of 16 experts in prostate AI, radiology and urology —to unify and standardize present-day guidelines, and to ensure meaningful validation of prostate AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p>
Zenodo
2022-06-10
info:eu-repo/semantics/other
6517397
user-grand-challenge
user-eu
v2.0
award_title=An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum; award_number=952159; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/952159; funder_id=00k4n6c32; funder_name=European Commission;
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doi
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