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Artificial Intelligence Software (Prostate Intelligence) for Detecting Clinically Significant Prostate Cancer on MRI
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Summary
The goal of this study is to learn how accurately an artificial intelligence (AI) software called Prostate Intelligence (Pi) detects clinically significant prostate cancer on prostate MRI in adults with a prostate gland who are referred for MRI because prostate cancer is suspected. The main question it aims to answer is: \- How sensitive and specific is Pi, used on its own, in detecting clinically significant prostate cancer (Gleason grade 3+4 or higher) at the patient level? Researchers will also compare Pi, the radiologist alone, and the radiologist assisted by Pi, to see whether AI assistance changes cancer detection. Participants will: * Undergo prostate MRI as part of their usual clinical care. Each MRI is first read by a radiologist without the AI result; the Pi report is then shown to the radiologist, who produces a final combined report. * Undergo targeted prostate biopsy (up to two lesions) if the MRI is suspicious, as in routine practice. Areas flagged only by Pi may also be biopsied. * If not biopsied, be followed for 12 months after MRI with PSA tests at 6 and 12 months and clinical assessment, to confirm the absence of clinically significant cancer. About 226 participants will be enrolled at two Italian centres (Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, and IRCCS Ospedale San Raffaele, Milan).
Official title: A Prospective, Multi-Centre Diagnostic Accuracy Study of AI-based Prostate Cancer Diagnosis Software
Key Details
Gender
MALE
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
226
Start Date
2026-11
Completion Date
2027-07
Last Updated
2026-10-15
Healthy Volunteers
No
Conditions
Interventions
AI-based prostate MRI analysis software
Prostate Intelligence (Pi, Lucida Medical Ltd) is an artificial intelligence software that analyses prostate MRI and provides prostate volume, lesion detection and segmentation, an AI-Likert score and a preliminary report. It runs on the participating institutions' servers on de-identified DICOM data. Each MRI is first reported by the radiologist blinded to Pi (report locked and time-stamped); Pi output is then revealed and the radiologist issues a merged Pi-assisted report (PI-RADS v2.1), accepting or rejecting each Pi finding. Up to two lesions per participant (radiologist- and/or Pi-identified) undergo MRI-targeted biopsy, with sampling order randomised 1:1. Non-biopsied participants are followed for 12 months (PSA at 6 and 12 months, clinical assessment).
Locations (1)
Fondazione Policlinico Universitario A. Gemelli IRCCS
Rome, Lazio, Italy