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Updated: Jan 18, 2026

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

Artificial Intelligence-Assisted Biparametric MRI for Detecting Prostate Cancer-A Comparative Multireader Multicase

Daniel Nißler¹, Sabrina Reimers-Kipping², Maja Ingwersen¹

¹Institute of Diagnostic and Interventional Radiology, Friedrich-Schiller-University Jena, Jena University Hospital, 07747 Jena, Germany.

Journal of Clinical Medicine

|September 13, 2025

Summary

This summary is machine-generated.

AI-assisted biparametric MRI (AI-bpMRI) shows diagnostic accuracy comparable to multiparametric MRI (mpMRI) for prostate cancer detection. This AI approach may offer a viable alternative, especially for less experienced readers.

Keywords:

artificial intelligence biparametric MRI computer-assisted diagnosis deep learning multiparametric magnetic resonance imaging prostate cancer

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Oncology Diagnostics

Background:

Multiparametric MRI (mpMRI) is standard for prostate cancer (PCa) detection.
Biparametric MRI (bpMRI) offers a streamlined approach but may have limitations.
AI integration aims to enhance diagnostic performance and accessibility.

Purpose of the Study:

To assess the diagnostic accuracy of AI-assisted bpMRI (AI-bpMRI) for PCa detection.
To compare AI-bpMRI with traditional mpMRI and standard bpMRI.
To evaluate the impact of reader experience on AI-bpMRI performance.

Main Methods:

Retrospective analysis of 105 men with suspected PCa.
Independent scoring of bpMRI, mpMRI, and AI-bpMRI by radiologists of varying experience.
PI-RADS V2.1 classification and histopathological correlation.
Area Under the Curve (AUC) analysis for diagnostic accuracy, with non-inferiority testing.

Main Results:

AI-bpMRI demonstrated non-inferiority to mpMRI in detecting clinically significant PCa (Gleason score ≥ 3+4).
AI-bpMRI was superior to bpMRI in detecting any PCa (Gleason score ≥ 3+3).
The diagnostic benefits of AI-bpMRI were most pronounced in radiologists with lower to medium experience.

Conclusions:

AI-bpMRI achieves diagnostic accuracy comparable to mpMRI for prostate cancer detection.
AI-bpMRI represents a promising alternative to mpMRI, potentially improving consistency across different experience levels.
Further validation may support AI-bpMRI as a cost-effective and efficient tool in PCa diagnostics.