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

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A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion.

Pegah Khosravi^1,2,3, Maria Lysandrou⁴, Mahmoud Eljalby⁵

¹Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Journal of Magnetic Resonance Imaging : JMRI

|March 15, 2021

Summary

This summary is machine-generated.

An AI-biopsy model aids in early prostate cancer diagnosis using MRI scans, potentially reducing unnecessary invasive procedures. This artificial intelligence tool analyzes magnetic resonance images to assess cancer risk, improving diagnostic strategies.

Keywords:

MRI images PI-RADS artificial intelligence biopsy deep neural networks 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:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Prostate cancer diagnosis traditionally relies on invasive biopsies, which carry risks and complications.
There is a need for less invasive and more accurate methods for early prostate cancer detection.

Purpose of the Study:

To develop and evaluate an artificial intelligence (AI)-based model, named AI-biopsy, for early prostate cancer diagnosis using magnetic resonance (MR) images.
The AI-biopsy model aims to distinguish between benign and cancerous tumors, and between high-risk and low-risk prostate cancer.

Main Methods:

A retrospective study utilized MR imaging datasets from 400 patients with suspected prostate cancer, including histopathology data.
Deep learning models were trained on MR images labeled with biopsy results (Gleason Score/Grade Group).
Model performance was evaluated using metrics such as area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

Main Results:

The AI-biopsy model achieved an AUC of 0.89 for classifying cancer versus benign conditions and 0.78 for distinguishing high-risk from low-risk prostate cancer.
The model demonstrated high performance in identifying cancerous regions within MR images.

Conclusions:

AI-biopsy offers a data-driven, reproducible method for assessing prostate cancer risk from MR images.
This AI tool can potentially reduce the number of unnecessary biopsies and aid in personalized diagnostic strategies.
The AI-biopsy system provides real-time analysis with a user-friendly interface for radiologists.