Andrey Bazarkin, Andrey Morozov, Alexander Androsov, Harun Fajkovic, Juan Gomez Rivas, Nirmish Singla, Svetlana Koroleva, Jeremy Yuen-Chun Teoh, Andrei V. Zvyagin, Shahrokh François Shariat, Bhaskar Somani, Dmitry Enikeev
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The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy.</p><h3 data-test=\"abstract-sub-heading\">Summary</h3><p>The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. 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Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review
Purpose of Review
The aim of the systematic review is to assess AI’s capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice.
Recent Findings
In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy.
Summary
The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.
期刊介绍：
This journal intends to review the most important, recently published findings in the field of urology. By providing clear, insightful, balanced contributions by international experts, the journal elucidates current and emerging approaches to the care and prevention of urologic diseases and conditions.
We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas, such as benign prostatic hyperplasia, erectile dysfunction, female urology, and kidney disease. Section Editors, in turn, select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. An international Editorial Board reviews the annual table of contents, suggests articles of special interest to their country/region, and ensures that topics are current and include emerging research. Commentaries from well-known figures in the field are also provided.
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