Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review
Usman Khalid,
Jasmin Gurung,
Mladen Doykov,
Gancho Kostov,
Bozhidar Hristov,
Petar Uchikov,
Maria Kraeva,
Krasimir Kraev,
Daniel Doykov,
Katya Doykova,
Siyana Valova,
Lyubomir Chervenkov,
Eduard Tilkiyan,
Krasimira Eneva
Affiliations
Usman Khalid
Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Jasmin Gurung
Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Mladen Doykov
Department of Urology and General Medicine, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Gancho Kostov
Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Bozhidar Hristov
Second Department of Internal Diseases, Section “Gastroenterology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Petar Uchikov
Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Maria Kraeva
Department of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Krasimir Kraev
Department of Propedeutics of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Daniel Doykov
Second Department of Internal Diseases, Section “Gastroenterology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Katya Doykova
Department of Diagnostic Imaging, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Siyana Valova
Second Department of Internal Diseases, Section “Nephrology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Lyubomir Chervenkov
Department of Diagnostic Imaging, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Eduard Tilkiyan
Second Department of Internal Diseases, Section “Nephrology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
Krasimira Eneva
Department of Infectious diseases, Parasitology and Tropical medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
The development of the Gleason grading system has proven to be an irreplaceable tool in prostate cancer diagnostics within urology. Despite the advancements and developments in diagnostics, there remains a discrepancy in the grading process among even the most experienced pathologists. AI algorithms have demonstrated potential in detecting cancer and assigning Gleason grades, offering a solution to the issue of significant variability among pathologists’ evaluations. Our paper explores the evolving role of AI in prostate cancer histopathology, with a key focus on outcomes and the reliability of various AI algorithms for Gleason pattern assessment. We conducted a non-systematic review of the published literature to examine the role of artificial intelligence in Gleason pattern diagnostics. The PubMed and Google Scholar databases were searched to gather pertinent information about recent advancements in artificial intelligence and their impact on Gleason patterns. We found that AI algorithms are increasingly being used to identify Gleason patterns in prostate cancer, with recent studies showing promising advancements that surpass traditional diagnostic methods. These findings highlight AI’s potential to be integrated into clinical practice, enhancing pathologists’ workflows and improving patient outcomes. The inter-observer variability in Gleason grading has seen an improvement in efficiency with the implementation of AI. Pathologists using AI have reported successful outcomes, demonstrating its effectiveness as a supplementary tool. While some refinements are still needed before AI can be fully implemented in clinical practice, its positive impact is anticipated soon.
