Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review
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...
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MDPI AG
2024-09-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/19/2127 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Usman Khalid |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3349981992ea453eb56a8d6436cde6eb |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-3349981992ea453eb56a8d6436cde6eb2025-08-20T01:47:44ZengMDPI AGDiagnostics2075-44182024-09-011419212710.3390/diagnostics14192127Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive ReviewUsman Khalid0Jasmin Gurung1Mladen Doykov2Gancho Kostov3Bozhidar Hristov4Petar Uchikov5Maria Kraeva6Krasimir Kraev7Daniel Doykov8Katya Doykova9Siyana Valova10Lyubomir Chervenkov11Eduard Tilkiyan12Krasimira Eneva13Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaMedical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Urology and General Medicine, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaSecond Department of Internal Diseases, Section “Gastroenterology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Propedeutics of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaSecond Department of Internal Diseases, Section “Gastroenterology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Diagnostic Imaging, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaSecond Department of Internal Diseases, Section “Nephrology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Diagnostic Imaging, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaSecond Department of Internal Diseases, Section “Nephrology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, BulgariaDepartment of Infectious diseases, Parasitology and Tropical medicine, Medical University of Plovdiv, 4002 Plovdiv, BulgariaThe 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.https://www.mdpi.com/2075-4418/14/19/2127artificial intelligenceGleason patternprostate cancerhistopathology |
| spellingShingle | 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 Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review Diagnostics artificial intelligence Gleason pattern prostate cancer histopathology |
| title | Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review |
| title_full | Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review |
| title_fullStr | Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review |
| title_full_unstemmed | Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review |
| title_short | Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review |
| title_sort | artificial intelligence algorithms and their current role in the identification and comparison of gleason patterns in prostate cancer histopathology a comprehensive review |
| topic | artificial intelligence Gleason pattern prostate cancer histopathology |
| url | https://www.mdpi.com/2075-4418/14/19/2127 |
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