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

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Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks.

Avtantil Dimitriadis^1,2,3, Grigorios Kalliatakis¹, Richard Osuala²

¹Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece.

Journal of Imaging

|April 25, 2025

Summary

This study introduces a novel deep learning model for prostate cancer (PCa) diagnosis using bi-parametric MRI. The model accurately distinguishes PCa presence from non-cancerous findings, achieving a 0.91 AUC.

Keywords:

cross-attention deep learning prostate cancer

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Prostate cancer (PCa) is a leading cause of cancer death in men, necessitating accurate diagnostic methods.
Current diagnostic approaches for PCa using magnetic resonance imaging (MRI) often focus on lesion detection or classification, not the presence of cancer itself.
Distinguishing between PCa and benign conditions from MRI is a critical but underexplored step in the clinical workflow.

Purpose of the Study:

To develop and evaluate a deep learning model for the first time on a large-scale dataset to differentiate between the presence and absence of prostate cancer using MRI.
To address the gap in current research by focusing on the critical task of cancer presence determination in the PCa clinical workflow.

Main Methods:

Utilized the multi-centric ProstateNET Imaging Archive, the largest collection of PCa MR images, comprising over 6 million representations from 11,000+ cases.
Trained a multi-encoder-cross-attention-fusion architecture on bi-parametric MR (bpMRI) images and clinical variables from 4504 patients.
Evaluated the model's performance on two independent test sets: 975 retrospective and 435 prospective patients.

Main Results:

Achieved a high area under the receiver operating characteristic curve (AUC) of 0.91, indicating strong diagnostic performance.
Demonstrated the model's capability to effectively fuse information from complex bi-parametric imaging modalities.
Showcased enhanced model robustness in distinguishing PCa presence across diverse patient cohorts.

Conclusions:

The developed deep learning model shows significant promise for accurately determining the presence of prostate cancer from MRI.
The method's ability to fuse multi-modal imaging data enhances robustness, supporting its potential for clinical adoption.
This work paves the way for improved diagnostic accuracy and reduced mortality through advanced AI in oncology.