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Published June 19, 2022 | Version 1.1
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Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)

  • 1. Department of Medical Imaging, Radboud University Medical Center, The Netherlands
  • 2. Department of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, The Netherlands
  • 3. Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Norway
  • 4. Department of Radiology, Ziekenhuis Groep Twente, The Netherlands
  • 1. Department of Medical Imaging, Radboud University Medical Center, The Netherlands
  • 2. Department of Urology, Skåne University Hospital, Sweden
  • 3. Division of Radiology, DKFZ, Germany
  • 4. Urology Unit, Santa Maria della Misericordia University Hospital, Italy
  • 5. Quantitative Translational Imaging in Medicine Lab, Harvard Medical School - Massachusetts General Hospital, USA
  • 6. Department of Urology, UCL Hospitals NHS Foundation Trust, UK
  • 7. Division of Medical Image Computing, DKFZ, Germany
  • 8. Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, UK
  • 9. Diagnostic Medicine and Radiology, Policlinico Umberto I, Italy
  • 10. Department of Urinary and Vascular Imaging, Hospices Civils de Lyon, France
  • 11. Laboratory for Integrative Personalized Medicine, Stanford University, USA
  • 12. Martini-Klinik, University Hospital Hamburg-Eppendorf, Germany
  • 13. Department of Urology, Saint Antonius Hospital, The Netherlands
  • 14. Department of Radiology and Nuclear Medicine, Ghent University Hospital, Belgium

Description

This document represents the preregistration of the PI-CAI challenge study design, in compliance with MICCAI-BIAS reporting guidelines.

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.

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 (Reinke et al., 2021).

 

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Additional details

Funding

ProCAncer-I – An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum 952159
European Commission