AccScience Publishing / AIH / Volume 1 / Issue 1 / DOI: 10.36922/aih.1958
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REVIEW

A bibliometric analysis of using machine learning and artificial intelligence in prostate cancer detection

Syed Asif Raza1* Nadeem Pervez2 Ikram A. Burney2 Momena Ahmed3
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1 Department of Marketing and Business Analytics, Texas A&M University-Commerce, Texas, USA
2 Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat, Oman
3 Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA
AIH 2024, 1(1), 3–15; https://doi.org/10.36922/aih.1958
Submitted: 30 September 2023 | Accepted: 23 December 2023 | Published: 26 December 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Prostate cancer stands as one of the most prevalent cancers globally among men, exhibiting substantial geographical variations in both incidence and mortality. While developed countries bear a higher incidence, developing countries grapple with elevated mortality rates. The heightened mortality in the latter is attributed to variations in practices that impede early diagnosis. In this context, the integration of artificial intelligence (AI) and machine learning (ML) has become increasingly common to improve the diagnostic accuracy of prostate cancer. This review delves into the existing literature to scrutinize the utilization of AI and ML in the diagnosis of prostate cancer. To compile relevant literature, comprehensive searches were conducted on research databases, including SCOPUS, Web of Science, and Google Scholar, to identify articles related to AI or ML (AI/ML) in the diagnosis and management of prostate cancer. Using a screening criterion, 293 reviewed research papers were identified. The two most consistent themes were predictive modeling and the application of AI/ML tools for cancer grading and radiomics. AI and ML enhance diagnostic accuracy by reducing inter-individual variation in Gleason’s scoring and complimenting the interpretation of multiparametric magnetic resonance imaging (mpMRI). A few publications reported the use of AI/ML tools that combine histopathology with MRI signals. The literature surveyed indicates a compelling potential for AI and ML to improve diagnostic accuracy in prostate cancer. Emerging literature suggests the use of a combination of demographic features, clinical data, serological markers, pathological grading and radiological factors, and genomic data to propose an accurate, non-invasive diagnosis of clinically significant prostate cancer.

Keywords
Prostrate cancer
Biopsy
Machine learning
Artificial intelligence
Bibliometrics analysis
Network and content analysis
Magnetic resonance imaging
Gleason score
Funding
None.
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Conflict of interest
The authors declare they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Published by AccScience Publishing