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Updated: May 1, 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

MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter

Young Joon Lee¹, Hyong Woo Moon², Moon Hyung Choi¹

¹Department of Radiology, Eunpyeong St Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021 Tongil-ro, Eunpyeong-gu, Seoul 03312, Republic of Korea.

|March 11, 2025

Summary

This summary is machine-generated.

A deep learning algorithm (DLA) showed lower sensitivity but higher positive predictive value for detecting clinically significant prostate cancer (csPCa) compared to radiologists. Integrating DLA with radiologist interpretations may improve diagnostic specificity while maintaining sensitivity.

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

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Published on: March 21, 2025

Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Prospective validation of artificial intelligence (AI) tools for prostate MRI is limited.
AI development for prostate cancer (PCa) detection is advancing rapidly.
Accurate PCa detection relies on robust diagnostic tools and interpretation.

Purpose of the Study:

To compare the diagnostic performance of a commercial deep learning algorithm (DLA) against radiologists' reports for PCa detection.
To evaluate the DLA's effectiveness using histopathology as the reference standard.
To assess the impact of DLA integration on diagnostic accuracy in a prospective, bicenter study.

Main Methods:

Prospective enrollment of participants with suspected PCa undergoing MRI and biopsy.
Comparison of Prostate Imaging Reporting and Data System (PI-RADS) scores from radiologists and DLA.
Analysis of diagnostic performance metrics including sensitivity, specificity, positive predictive value (PPV), and AUC.
Utilized histopathology from biopsy specimens as the gold standard for diagnosis.

Main Results:

The DLA demonstrated lower sensitivity (80%) but higher PPV (58%) for detecting clinically significant PCa (csPCa) per lesion compared to radiologists (93% sensitivity, 48% PPV).
Incorporating DLA into radiologist interpretations significantly increased specificity (21% to 44%) per participant, while maintaining high sensitivity (96% vs 99%).
No significant difference was observed in the area under the receiver operating characteristic curve (AUC) between radiologist-based and DLA-based PI-RADS scores (0.77 vs 0.79).

Conclusions:

The DLA exhibits a different performance profile than radiologists, with lower sensitivity but higher PPV for csPCa detection.
Combining DLA findings with radiologist interpretations, particularly for indeterminate PI-RADS 3 scores, can enhance diagnostic specificity.
AI tools like the DLA hold potential to augment radiologist performance in prostate cancer diagnosis, improving overall accuracy.