Peer-Reviewed Publication
Eur Radiol2026September 26, 2026Journal Article
Multicentre evaluation of artificial intelligence risk classification for detection of clinically significant prostate cancer on biparametric MRI.
Karsten Guenzel1,2,3, Maarten G Poirot4, Almar van Loon4, Emanuele Messina5, Martina Pecoraro5, Valeria Panebianco5, Robert Princenthal6,7, Francesco Giganti8,9
1Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany.
2Prostate-Diagnostic-Centre Berlin, PDZB, Berlin, Germany.
3Department of Urology, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany.
4DeepHealth Inc., Somerville, MA, USA.
5Department of Radiological Sciences, Oncology and Pathology, Sapienza University, Rome, Italy.
6Rolling Oaks Radiology, Thousand Oaks, CA, USA.
7RadNet, Inc., Thousand Oaks, CA, USA.
8Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK. f.giganti@ucl.ac.uk.
9Division of Surgery & Interventional Science, University College London, London, UK. f.giganti@ucl.ac.uk.
Abstract
OBJECTIVES: To compare a commercially available artificial intelligence (AI) risk-classification system with PI-RADS for detecting clinically significant prostate cancer (csPCa) on prostate MRI.
MATERIALS AND METHODS: This retrospective multicentre, multivendor diagnostic accuracy study included men who underwent prostate MRI and histopathologic verification between 2014 and 2025. Index tests wer…
Create a free account to keep reading
Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.
Want unlimited research access? See Pro plans