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Updated: Aug 26, 2025

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

Improving Prostate Cancer Detection With MRI: A Multi-Reader, Multi-Case Study Using Computer-Aided Detection (CAD).

Mark A Anderson¹, Sarah Mercaldo², Ryan Chung¹

¹Department of Radiology, Abdominal Imaging Division, Massachusetts General Hospital, 55 Fruit Street, White 270, Boston, MA 02114; Harvard Medical School, 55 Fruit Street, White 270, Boston, MA 02114.

Academic Radiology

|October 10, 2022

Summary

This summary is machine-generated.

Computer-aided diagnosis (CAD) MRI significantly improved detection of clinically significant prostate cancer. This AI tool enhanced radiologist agreement and diagnostic accuracy for prostate cancer MRI interpretation.

Keywords:

bi-parametric prostate MRI computer-aided diagnosis magnetic resonance imaging prostate adenocarcinoma random forest model

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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
Medical Imaging
Artificial Intelligence in Medicine

Background:

Prostate cancer detection relies heavily on MRI interpretation.
Accurate diagnosis of clinically significant prostate cancer is crucial for patient management.
Inter-reader variability can impact diagnostic accuracy in prostate MRI.

Purpose of the Study:

To determine if a computer-aided diagnostic (CAD) generated MRI series enhances the detection of clinically significant prostate cancer.
To assess the impact of CAD on radiologist performance and agreement in prostate MRI.

Main Methods:

Nine radiologists retrospectively reviewed 150 prostate MRI exams, with and without an additional CAD-generated series.
Diagnostic performance was compared using the area under the receiver operator characteristic curve (AUC).
Inter-reader agreement (IRA) for PI-RADS scores was evaluated.

Main Results:

The addition of CAD significantly improved inter-reader agreement from moderate to substantial (IRA=0.47 to 0.65, p < .001).
Average reader AUC also significantly increased with CAD (AUC=0.72 vs. 0.67, p = .02).
CAD demonstrated particular benefit in the transition zone.

Conclusions:

A random forest-based CAD system enhances MRI interpretation for clinically significant prostate cancer.
CAD improves both inter-reader agreement and diagnostic performance in prostate cancer detection.
The use of CAD in prostate MRI shows promise for improving diagnostic accuracy.