urovestВестник урологииUrology Herald2308-6424Rostov State Medical University10.21886/2308-6424-2024-12-1-117-130urovest-836Research ArticleОБЗОРЫ ЛИТЕРАТУРЫREVIEWS ARTICLEПрименение искусственного интеллекта в молекулярной и генетической диагностике рака простатыArtificial intelligence in molecular and genomic prostate cancer diagnosticshttps://orcid.org/0000-0001-6694-837XМорозовА. О.MorozovA. O.

Андрей Олегович Морозов — канд. мед. наук; врач-уролог, старший научный сотрудник Института урологии и репродуктивного здоровья человека

Москва

Andrey O. Morozov — M.D., PhD; Urologist & Senior Researcher, Institute for Urology and Reproductive Health

Moscow

andrei.o.morozov@gmail.comhttps://orcid.org/0000-0003-3552-4851БазаркинА. К.BazarkinA. K.

Андрей Константинович Базаркин — врач-ординатор, стажёр-исследователь Института урологии и репродуктивного здоровья человека

Москва

Andrey K. Bazarkin — M.D.; resident, Researcher Assistant, Institute for Urology and Reproductive Health

Moscow

ak.bazarkin@gmail.comhttps://orcid.org/0000-0001-6606-147XВовденкоС. В.VovdenkoS. V.

Станислав Викторович Вовденко — врач-уролог, научный сотрудник Института урологии и репродуктивного здоровья человека

Москва

Stanislav V. Vovdenko — M.D., Urologist & Researcher; Institute for Urology and Reproductive Health

Moscow

vovdenkostanislav@yandex.ruhttps://orcid.org/0000-0003-4369-173XТараткинМ. С.TaratkinM. S.

Марк Сергеевич Тараткин — врач-уролог, научный сотрудник Института урологии и репродуктивного здоровья человека

Москва

Mark S. Taratkin — M.D., Urologist & Researcher, Institute for Urology and Reproductive Health

Moscow

marktaratkin@gmail.comhttps://orcid.org/0000-0002-5117-3580БалашоваМ. С.BalashovaM. S.

Мария Сергеевна Балашова — канд. мед. наук; врач-генетик, доцент кафедры медицинской генетики

Москва

Maria S. Balashova — M.D., PhD; Geneticist & Assoc.Prof., Dept. of Medical Genetics

Moscow

zimt@list.comhttps://orcid.org/0000-0001-7169-2209ЕникеевД. В.EnikeevD. V.

Дмитрий Викторович Еникеев — д-р мед. наук, профессор; профессор Института урологии и репродуктивного здоровья человека; Доцент кафедры урологии; доцент; Старший хирург

Москва

Вена, Австрия

Петах Тиква, Израиль

   

Dmitry V. Enikeev — M.D., Dr.Sc.(Med), Full Prof.; Prof., Institute for Urology and Reproductive Health); Adjunct Prof., Dept. of Urology and Comprehensive Cancer Centre; Adjunct Prof.; Senior surgeon, Division of Urology

Moscow

Vienna, Austria

Petah Tikva, Israel

dvenikeev@gmail.comПервый Московский государственный медицинский университет им. И. М. Сеченова (Сеченовский Университет)РоссияSechenov First Moscow State Medical University (Sechenov University)Russian FederationПервый Московский государственный медицинский университет им. И. М. Сеченова (Сеченовский Университет); Венский медицинский университет; Университет здравоохранения им. Карла Ландштейнера — Институт урологии и андрологии; Медицинский центр им. РабинаРоссияSechenov 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 CenterRussian Federation202405032024121117130Copyright © Морозов А.О., Базаркин А.К., Вовденко С.В., Тараткин М.С., Балашова М.С., Еникеев Д.В., 20242024Морозов А.О., Базаркин А.К., Вовденко С.В., Тараткин М.С., Балашова М.С., Еникеев Д.В.Morozov A.O., Bazarkin A.K., Vovdenko S.V., Taratkin M.S., Balashova M.S., Enikeev D.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.urovest.ru/jour/article/view/836Введение

Введение. Для прогнозирования течения рака предстательной железы (РПЖ) предложено множество молекулярно-генетических анализов. Их потенциал способны развить алгоритмы искусственного интеллекта (ИИ) за счёт обработки большого объёма данных и установления связей между ними.

Цель исследования

Цель исследования. Оценить возможности применения искусственного интеллекта в ранней диагностике и прогнозировании течения рака предстательной железы.

Материалы и методы

Материалы и методы. Проведён систематический обзор литературы по базе данных цитирования Medline. Отобраны статьи, представляющие данные о применении систем ИИ in vitro, in vivo и in silico для определения биологических, генетических маркеров и / или их связи с клиническими данными больных РПЖ во временном промежутке с 2020 по 2023 годы. В качественный синтез включены 16 статей.

Результаты

Результаты. ИИ способен определять метаболический и генетический «почерк» РПЖ, ключевые элементы сигнальных путей, выполняя таким образом сложные задачи в области биоинформатики. ИИ анализирует различный биоматериал: ткань простаты, кровь и мочу. При оценке ткани простаты на предмет аберраций ИИ может помочь врачу-патологоанатому: по гистологическому изображению ИИ может, например, предугадать экспрессионный статус генов, что устраняет необходимость в ИГХ или секвенировании ткани, значительно снижая экономические затраты для прогнозирования тяжести заболевания. В большинстве случаев секвенирование ткани простаты предоставляет соответствующую информацию для лечащего врача, позволяя начать оптимальное лечение, приняв во внимание молекулярный или генетический «почерк» РПЖ. ИИ может быть применён в качестве альтернативы существующим инструментам скрининга населения, а также выявить предикторы кастрационно-резистентной формы РПЖ. Применение возможностей ИИ целесообразнее для анализа крови и мочи — процедур, не требующих дополнительных экономических затрат на забор биоматериала. В теории это может быть экономически более доступно для пациента и медицинского учреждения. Стоит отметить, что ряд исследований проведено in silico (на основании анализа молекулярно-генетических баз данных без валидации на клеточных линиях или на реальных пациентах) и полезны скорее, как справочная информация. Тем не менее полученные результаты могут служить надежной базой для дальнейших исследований в области молекулярной диагностики и геномики.

Заключение

Заключение. При диагностике РПЖ возможно применение ИИ при поиске ключевых метаболитов и генов элементов сигнальных путей, а также определение потенциала метастазирования, поскольку молекулярный или генетический «почерк» рака простаты позволяет врачу начать оптимальное лечение.

Introduction

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

Objective. To evaluate the possibilities of using artificial intelligence in early diagnosis and prognosis of prostate cancer.

Materials & methods

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

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

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.

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The authors declare that there are no conflicts of interest present.