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World J Radiol. May 28, 2023; 15(5): 136-145
Published online May 28, 2023. doi: 10.4329/wjr.v15.i5.136
Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging
Lyubomir Chervenkov, Nikolay Sirakov, Gancho Kostov, Tsvetelina Velikova, George Hadjidekov
Lyubomir Chervenkov, Department of Diagnostic Imaging, Medical University Plovdiv, Plovdiv 4000, Bulgaria
Lyubomir Chervenkov, Nikolay Sirakov, Research Complex for Translational Neuroscience, Medical University of Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4002, Bulgaria
Nikolay Sirakov, Department of Diagnostic Imaging, Dental Allergology and Physiotherapy, Faculty of Dental Medicine, Medical University Plovdiv, Plovdiv 4000, Bulgaria
Gancho Kostov, Department of Special Surgery, Medical University Plovdiv, Plovdiv 4000, Bulgaria
Tsvetelina Velikova, Department of Clinical Immunology, University Hospital Lozenetz, Sofia 1407, Bulgaria
Tsvetelina Velikova, Department of Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
George Hadjidekov, Department of Radiology, University Hospital Lozenetz, Sofia 1407, Bulgaria
George Hadjidekov, Department of Physics, Biophysics and Radiology, Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
Author contributions: Chervenkov L, Velikova T and Hadjidekov G conceptualized the study; Chervenkov L, Sirakov N and Kostov G designed the methodology; Chervenkov L performed the data curation; Chervenkov L prepared the original draft of the manuscript; Velikova T and Hadjidekov G reviewed and edited the manuscript for intellectual content; All authors contributed to manuscript revision and provided approval of the final version of the manuscript to be published.
Supported by the European Union’s NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, Project No. BG-RRP-2.004-0008-C01.
Conflict-of-interest statement: All the authors report having no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: George Hadjidekov, MD, PhD, Associate Professor, Department of Radiology, University Hospital Lozenetz, 1 Kozyak Street, Sofia 1407, Bulgaria. jordiman76@yahoo.com
Received: December 19, 2022
Peer-review started: December 19, 2022
First decision: February 20, 2023
Revised: March 21, 2023
Accepted: April 10, 2023
Article in press: April 10, 2023
Published online: May 28, 2023
Processing time: 154 Days and 15.8 Hours
Abstract

Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca.

Keywords: Artificial intelligence; Deep learning; Machine learning; Multiparametric magnetic resonance imaging; Prostate cancer; Quantitative imaging

Core Tip: The peer reviewed literature has provided sufficient support for the continued application and development of artificial intelligence (AI) in prostate cancer clinical care. In addition, the expanding introduction of various AI-based software products created by leading companies is providing practical benefits to radiologists for improved prostate cancer diagnosis. Certainly, the known complexity of the disease and its consequential difficult diagnosis supports the continued development of new approaches for earlier and more accurate detection, such as could be provided through AI technologies.