Artificial intelligence in molecular and genomic prostate cancer diagnostics
Introduction. Many molecular genetic analyses have been proposed to predict the course of prostate cancer (PCa). They have the potential to develop artificial intelligence (AI) algorithms by processing large amounts of data and define connections between them.Objective. To evaluate the possibilities...
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| Format: | Article |
| Language: | Russian |
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Ministry of Health of Russian Federation, Rostov State Medical University, State Budget Educational Institute of Higher Professional Education
2024-03-01
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| Series: | Вестник урологии |
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| Online Access: | https://www.urovest.ru/jour/article/view/836 |
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| author | A. O. Morozov A. K. Bazarkin S. V. Vovdenko M. S. Taratkin M. S. Balashova D. V. Enikeev |
| author_facet | A. O. Morozov A. K. Bazarkin S. V. Vovdenko M. S. Taratkin M. S. Balashova D. V. Enikeev |
| author_sort | A. O. Morozov |
| collection | DOAJ |
| description | Introduction. Many molecular genetic analyses have been proposed to predict the course of prostate cancer (PCa). They have the potential to develop artificial intelligence (AI) algorithms by processing large amounts of data and define connections between them.Objective. To evaluate the possibilities of using artificial intelligence in early diagnosis and prognosis of prostate cancer.Materials & methods. We conducted a systematic review of the literature on the Medline citation database. We have selected papers that provide data on the use of AI in vitro, in vivo and in silico systems to determine biological and genetic markers and/or their relationship to clinical data of PCa-patients from 2020 to 2023. The quantitative synthesis includes 16 articles.Results. AI can identify metabolic and genetic «signature» of PCa, the key elements of signal pathways, thus fulfilling complex tasks in the field of bioinformatics. AI analyses various biomaterials: prostate tissue, blood, and urine. When evaluating prostate tissue for aberrations, AI can help a pathologist. For example, AI can predict the histological status of genes, eliminating the need for IHC or tissue sequencing, significantly reducing the economic cost of predicting the severity of the disease. In most cases, prostate tissue sequencing provides information to the attending physician, allowing the start of optimal treatment, considering the molecular or genetic «signature» of PCa. AI can be used as an alternative to existing population screening tools and a predictive castration-resistant PCa. The use of AI capabilities is more appropriate for blood and urine analysis, procedures that do not require additional economic costs for biomaterial sampling. In theory, this may be more affordable for the patient and the medical institution. It is worth noting that a few studies were conducted in silico (based on the analysis of molecular genetic databases without validation on cell lines or on real patients) and are useful as background information. However, the results can serve as a robust basis for further research in molecular diagnostics and genomics.Conclusion. It is possible to use AI in the search for key metabolites and genes of the elements of signalling pathways, as well as the determination of metastasis potential, because molecular or genetic «signature» of PCa allows the physician to start optimal treatment. |
| format | Article |
| id | doaj-art-b1b03a9af9054fd29319e4a269d84fbc |
| institution | Kabale University |
| issn | 2308-6424 |
| language | Russian |
| publishDate | 2024-03-01 |
| publisher | Ministry of Health of Russian Federation, Rostov State Medical University, State Budget Educational Institute of Higher Professional Education |
| record_format | Article |
| series | Вестник урологии |
| spelling | doaj-art-b1b03a9af9054fd29319e4a269d84fbc2025-08-20T03:57:21ZrusMinistry of Health of Russian Federation, Rostov State Medical University, State Budget Educational Institute of Higher Professional EducationВестник урологии2308-64242024-03-0112111713010.21886/2308-6424-2024-12-1-117-130508Artificial intelligence in molecular and genomic prostate cancer diagnosticsA. O. Morozov0A. K. Bazarkin1S. V. Vovdenko2M. S. Taratkin3M. S. Balashova4D. V. Enikeev5Sechenov First Moscow State Medical University (Sechenov University)Sechenov First Moscow State Medical University (Sechenov University)Sechenov First Moscow State Medical University (Sechenov University)Sechenov First Moscow State Medical University (Sechenov University)Sechenov First Moscow State Medical University (Sechenov University)Sechenov First Moscow State Medical University (Sechenov University); Medical University of Vienna (MedUni Vienna); Karl Landsteiner University of Health Sciences — Institute of Urology and Andrology; Rabin Medical CenterIntroduction. Many molecular genetic analyses have been proposed to predict the course of prostate cancer (PCa). They have the potential to develop artificial intelligence (AI) algorithms by processing large amounts of data and define connections between them.Objective. To evaluate the possibilities of using artificial intelligence in early diagnosis and prognosis of prostate cancer.Materials & methods. We conducted a systematic review of the literature on the Medline citation database. We have selected papers that provide data on the use of AI in vitro, in vivo and in silico systems to determine biological and genetic markers and/or their relationship to clinical data of PCa-patients from 2020 to 2023. The quantitative synthesis includes 16 articles.Results. AI can identify metabolic and genetic «signature» of PCa, the key elements of signal pathways, thus fulfilling complex tasks in the field of bioinformatics. AI analyses various biomaterials: prostate tissue, blood, and urine. When evaluating prostate tissue for aberrations, AI can help a pathologist. For example, AI can predict the histological status of genes, eliminating the need for IHC or tissue sequencing, significantly reducing the economic cost of predicting the severity of the disease. In most cases, prostate tissue sequencing provides information to the attending physician, allowing the start of optimal treatment, considering the molecular or genetic «signature» of PCa. AI can be used as an alternative to existing population screening tools and a predictive castration-resistant PCa. The use of AI capabilities is more appropriate for blood and urine analysis, procedures that do not require additional economic costs for biomaterial sampling. In theory, this may be more affordable for the patient and the medical institution. It is worth noting that a few studies were conducted in silico (based on the analysis of molecular genetic databases without validation on cell lines or on real patients) and are useful as background information. However, the results can serve as a robust basis for further research in molecular diagnostics and genomics.Conclusion. It is possible to use AI in the search for key metabolites and genes of the elements of signalling pathways, as well as the determination of metastasis potential, because molecular or genetic «signature» of PCa allows the physician to start optimal treatment.https://www.urovest.ru/jour/article/view/836artificial intelligencemachine learningprostate cancermolecular diagnosticsgenetical diagnosticsreview |
| spellingShingle | A. O. Morozov A. K. Bazarkin S. V. Vovdenko M. S. Taratkin M. S. Balashova D. V. Enikeev Artificial intelligence in molecular and genomic prostate cancer diagnostics Вестник урологии artificial intelligence machine learning prostate cancer molecular diagnostics genetical diagnostics review |
| title | Artificial intelligence in molecular and genomic prostate cancer diagnostics |
| title_full | Artificial intelligence in molecular and genomic prostate cancer diagnostics |
| title_fullStr | Artificial intelligence in molecular and genomic prostate cancer diagnostics |
| title_full_unstemmed | Artificial intelligence in molecular and genomic prostate cancer diagnostics |
| title_short | Artificial intelligence in molecular and genomic prostate cancer diagnostics |
| title_sort | artificial intelligence in molecular and genomic prostate cancer diagnostics |
| topic | artificial intelligence machine learning prostate cancer molecular diagnostics genetical diagnostics review |
| url | https://www.urovest.ru/jour/article/view/836 |
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