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Updated: May 2, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

AI-powered prostate cancer detection: a multi-centre, multi-scanner validation study.

Francesco Giganti^1,2, Nadia Moreira da Silva³, Michael Yeung³

¹Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK. f.giganti@ucl.ac.uk.

European Radiology

|February 27, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) software demonstrated comparable performance to multidisciplinary team-supported radiologists in detecting significant prostate cancer (PCa) using multiparametric MRI. This AI tool shows generalizability across multiple sites and scanner vendors, supporting biopsy decision-making.

Keywords:

Artificial intelligence Magnetic resonance imaging Prostatic neoplasms

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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Multi-centre, multi-vendor validation of AI software for prostate cancer (PCa) detection using multiparametric MRI is limited.
Existing AI solutions require validation on diverse datasets to ensure generalizability.

Purpose of the Study:

To validate a new AI software (Pi) against multidisciplinary team (MDT)-supported radiologist interpretations for detecting clinically significant PCa.
To assess the AI's performance across multiple sites, vendors, and scanner models using real-world data.

Main Methods:

A CE-marked deep-learning (DL) computer-aided detection (CAD) device (Pi) was trained on retrospective data (793 patients) and validated on a separate dataset (252 patients) from six machines across six sites.
Radiologists' interpretations using a 5-category suspicion score were compared to AI performance via ROC analysis.

Main Results:

The AI (Pi) was non-inferior to radiologists in detecting Gleason Grade Group (GG) ≥2 PCa (AUC 0.91 vs. 0.95).
At a specific threshold, AI sensitivity was 95% (specificity 67%), while radiologists achieved 99% sensitivity (specificity 73%).
AI performance remained robust across different sites, scanner ages, and field strengths (AUC ≥0.83).

Conclusions:

Real-world data indicate that the AI software (Pi) matches the performance of MDT-supported radiologists in detecting GG≥2 PCa.
The AI tool demonstrates generalizability across multiple sites, scanner vendors, and models, supporting its potential clinical utility.
Further prospective studies are needed to evaluate AI-identified lesions in separate biopsy procedures.