The Open Artificial Intelligence Journal


ISSN: 1874-0618 ― Volume 6, 2020

Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence

Chris R. Nelson1, Jessica Ekberg2, Kent Fridell2, *
1 Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
2 Clinical Science, Interven on and technology (CLINTEC), Karolinska Institute, Stockholm, Sweden



Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer.


The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening.


This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results.


CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist.


When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.

Keywords: MRT, MRI, Prostate cancer, Computer-aided diagnosis, Artificial intelligence, Segmentation, Machine learning, Deep learning.

Article Information

Identifiers and Pagination:

Year: 2020
Volume: 6
First Page: 1
Last Page: 11
Publisher Id: TOAIJ-6-1
DOI: 10.2174/1874061802006010001

Article History:

Received Date: 16/10/2019
Revision Received Date: 22/12/2019
Acceptance Date: 16/01/2020
Electronic publication date: 20/03/2020
Collection year: 2020

© 2020 Nelson et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Clinical Science, Interven on and technology (CLINTEC), Karolinska Institute, Stockholm, Sweden;
Tel: +46706683870; E-mail:

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