Clinical trial · Observational
Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge
NCT05489341CI-TRIAL-00071502completedClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The PI-CAI challenge aims to validate the diagnostic performance of artificial intelligence (AI) and radiologists at clinically significant prostate cancer (csPCa) detection/diagnosis in MRI, with respect to histopathology and follow-up (≥ 3 years) as reference. The study hypothesizes that state-of-the-art AI algorithms, trained using thousands of patient exams, are non-inferior to radiologists reading bpMRI. As secondary end-points, it investigates the optimal AI model for csPCa detection/diagnosis, and the effects of dynamic contrast-enhanced imaging and reader experience on diagnostic accuracy and inter-reader variability.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Histopathology and Magnetic Resonance Imaging | Diagnostic Test | — | UNRESOLVED |
| Histopathology and Magnetic Resonance Imaging with Follow-Up | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (4)
- label
- Public Training and Development Set (1500 cases)
- description
- Available for all participants and researchers, to train and develop AI models. Includes multi-vendor (Siemens Healthineers, Philips Medical Systems) prostate bpMRI cases from three Dutch centers (Radboud University Medical Center, Ziekenhuisgroep Twente, University Medical Center Groningen), acquired between 2012-2021. All data is fully anonymized and made available under a non-commercial CC BY-NC 4.0 license. Includes 328 cases from the PROSTATEx challenge (prostatex.grand-challenge.org). Imaging data has been released via: zenodo.org/record/6624726 (DOI: 10.5281/zenodo.6624726). Lesion annotations of csPCa have been released and are maintained via: github.com/DIAGNijmegen/picai\_labels.
- interventionNames
- Diagnostic Test: Histopathology and Magnetic Resonance Imaging
- label
- Private Training Set (7500-9500 cases)
- description
- Used exclusively by the organizers to retrain the top-ranking 5 AI algorithms, with large-scale data. Includes multi-vendor (Siemens Healthineers, Philips Medical Systems) prostate bpMRI cases from three Dutch centers (Radboud University Medical Center, Ziekenhuisgroep Twente, University Medical Center Groningen), acquired between 2012-2021.
- interventionNames
- Diagnostic Test: Histopathology and Magnetic Resonance Imaging
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Men suspected of harboring csPCa, with elevated levels of prostate-specific antigen (≥ 3 ng/mL) and/or abnormal findings on digital rectal exam, who subsequently underwent prostate MRI. Exclusion Criteria: * Patients who opted-out or did not give permission to reuse clinical data. * Patients with a history of prior prostate treatment. * Patients with a history of prior positive csPCa findings in histopathology (ISUP ≥ 2). * Patients whose prostate MRI exhibit severe artifacts (e.g. heavy warping due to rectal air, metal artifacts from hip prostheses, heavy motion blur), thereby impeding their usage. * Patients, whose positive histopathology findings (ISUP ≥ 2) cannot be reliably localized on MRI (e.g. MRI-invisible lesions, systematic biopsy diagnostic reports with ambiguous, "random" or missing location information).
References
Publications (2)
- DERIVEDTwilt JJ, Saha A, Bosma JS, Giannarini G, Padhani AR, Yakar D, Elschot M, Veltman J, Futterer J, Huisman H, de Rooij M; PI-CAI Consortium. Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study. Radiol Imaging Cancer. 2026 May;8(3):e250461. doi: 10.1148/rycan.250461. PMID 41960994
- DERIVEDSaha A, Bosma JS, Twilt JJ, van Ginneken B, Bjartell A, Padhani AR, Bonekamp D, Villeirs G, Salomon G, Giannarini G, Kalpathy-Cramer J, Barentsz J, Maier-Hein KH, Rusu M, Rouviere O, van den Bergh R, Panebianco V, Kasivisvanathan V, Obuchowski NA, Yakar D, Elschot M, Veltman J, Futterer JJ, de Rooij M, Huisman H; PI-CAI consortium. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol. 2024 Jul;25(7):879-887. doi: 10.1016/S1470-2045(24)00220-1. Epub 2024 Jun 11. PMID 38876123