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

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Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI

Thomas Sanford¹, Stephanie A Harmon², Evrim B Turkbey³

¹Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.

Journal of Magnetic Resonance Imaging : JMRI

|June 2, 2020

Summary

An AI system for Prostate Imaging Reporting and Data System (PI-RADS) classification shows moderate agreement with expert radiologists. This AI demonstrates a similar ability to detect clinically significant prostate cancer.

Keywords:

MRI PI-RADS artificial intelligence deep learning prostate

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

The Prostate Imaging Reporting and Data System (PI-RADS) standardizes prostate lesion risk stratification using multiparametric MRI (mpMRI).
High intra/interreader variability in PI-RADS scoring limits its current clinical utility.

Purpose of the Study:

To develop an artificial intelligence (AI) solution for PI-RADS classification of prostate lesions.
To compare the AI's PI-RADS classification performance against an expert radiologist.

Main Methods:

A retrospective study utilized data from 687 patients with mpMRI scans and PI-RADS scores >1.
A convolutional neural network (CNN) was trained on segmented lesions from T2-weighted, DWI, and DCE-T1-weighted MRI sequences.
AI-generated PI-RADS scores were compared to radiologist scores, with cancer detection rates assessed via targeted biopsy in a subset of patients.

Main Results:

The AI system achieved a moderate kappa score of 0.40 in agreement with the expert radiologist for 1034 lesions.
No significant difference was observed in the detection rates of clinically significant prostate cancer between the AI and the radiologist in 86 patients undergoing biopsy.

Conclusions:

An AI system for PI-RADS scoring on mpMRI demonstrates moderate agreement with expert radiologists.
The developed AI shows comparable performance to expert radiologists in detecting clinically significant prostate cancer.