gnckКолопроктологияKoloproktologia2073-75562686-7303Russian Association of Coloproctology10.33878/2073-7556-2024-23-2-184-193gnck-1914Research ArticleОБЗОР ЛИТЕРАТУРЫREVIEWВозможности и перспективы применения искусственного интеллекта в лечении колоректального рака (обзор литературы)Possibilities and prospects of artificial intelligence in the treatment of colorectal cancer (review)https://orcid.org/0000-0002-5050-7446КравченкоА. Ю.KravchenkoA. Yu.

Кравченко Александр Юрьевич — к.м.н., заведующий кафедрой  организации общественного здоровья и здравоохранения Высшей школы медицины 

ул. А. Невского, д. 14, г. Калининград, 236041, Россия

A. Nevskogo st., 14, Kaliningrad, 236041, Russia

a.u.kravchenko@gmail.comhttps://orcid.org/0000-0002-3927-9286СеминаЕ. В.SeminaE. V.

Семина Екатерина Владимировна — д.б.н., заместитель руководителя по развитию и проектной деятельности образовательно-научного кластера«МЕДБИО»; ведущий научный сотрудник лаборатории морфогенеза и репарации тканей факультета фундаментальной медицины 

ул. А. Невского, д. 14, г. Калининград, 236041, Россия

ул. Ленинские горы, д. 1, г. Москва, 119991, Россия 

A. Nevskogo st., 14, Kaliningrad, 236041, Russia

Leninskie Gory st., 1, Moscow, 119991, Russia 

e-semina@yandex.ruhttps://orcid.org/0000-0003-0352-2317КакоткинВ. В.KakotkinV. V.

 Какоткин Виктор Викторович — ассистент кафедры хирургических дисциплин высшей школы медицины 

тел.: +7 (985)100-07-94

ул. А. Невского, д. 14, г. Калининград, 236041, Россия

Viktor V. Kakotkin 

A. Nevskogo st., 14, Kaliningrad, 236041, Russia

vkakotkin@kantiana.ruhttps://orcid.org/0000-0002-6569-7078АгаповМ. А.AgapovM. A.

Агапов Михаил Андреевич — д.м.н., руководитель образовательно-научного кластера; профессор кафедры хирургии факультета  фундаментальной медицины 

ул. А. Невского, д. 14, г. Калининград, 236041, Россия

ул. Ленинские горы, д. 1, г. Москва, 119991, Россия 

A. Nevskogo st., 14, Kaliningrad, 236041, Russia

Leninskie Gory st., 1, Moscow, 119991, Russia 

getinfo911@mail.ruФГАОУ ВО «Балтийский федеральный университет имени Иммануила Канта»РоссияImmanuel Kant Baltic Federal UniversityRussian FederationФГАОУ ВО «Балтийский федеральный университет имени Иммануила Канта»; ФГБОУ ВО «Московский государственный университет имени М.В. Ломоносова»РоссияImmanuel Kant Baltic Federal University; Lomonosov Moscow State UniversityRussian Federation202421062024232184193Copyright © Кравченко А.Ю., Семина Е.В., Какоткин В.В., Агапов М.А., 20242024Кравченко А.Ю., Семина Е.В., Какоткин В.В., Агапов М.А.Kravchenko A.Y., Semina E.V., Kakotkin V.V., Agapov M.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.ruproctology.com/jour/article/view/1914

ЦЕЛЬ: изучить современные подходы к применению технологий машинного обучения и глубокого обучения на различных этапах ведения больных колоректальным раком.МАТЕРИАЛЫ И МЕТОДЫ: проведен анализ опубликованных данных в National Library of Medicine (база данных Pubmed) за последние 5 лет. После скрининга 398 публикаций отобраны 112 статей, изучен полный текст работ. После изучения полных текстов статей были отобраны работы, модели машинного обучения в которых в ходе валидации показали точность более 80%. Для написания данной работы использованы результаты 41 оригинальной публикации.РЕЗУЛЬТАТЫ: удалось выделить несколько направлений, являющихся наиболее перспективными для применения технологий искусственного интеллекта при ведении больных колоректальным раком: прогнозирование ответа на неоадъювантное лечение, прогнозирование рисков отдаленного метастазирования и рецидивирования заболевания, прогнозирование токсичности химиотерапии, оценка рисков несостоятельности колоректальных анастомозов. В качестве наиболее перспективных факторов, которые могут быть использованы для обучения моделей, исследователи рассматривают клинические показатели, иммунное окружение опухоли, РНК-сигнатуры опухоли, а также визуальные патоморфологические характеристики. Наибольшей точностью характеризовались модели, предназначенные для предсказания риска метаститического поражения печени у пациентов с T1-стадией (AUC = 0,9631), а также модели, направленные на оценку риска 30-ти дневной летальности на фоне химиотерапии (AUC = 0,924). Большинство из обсуждаемых в работе технологий представляют из себя программные продукты, обученные на различных по качеству и количеству наборах данных, которые способны подсказать сценарий лечения на основе прогнозных моделей, и, по сути, могут выступать в качестве помощника врача с очень ограниченным функционалом.ЗАКЛЮЧЕНИЕ: текущий уровень развития цифровых технологий в онкологии, а именно в лечении КРР, не позволяет говорить о полноценном ИИ, способном принимать решения о лечении пациентов без врачебного контроля. Действительно персонифицированные схемы лечения, основанные на микробиотическом и мутационном спектре и, например, персональной фармакокинетике, пока что выглядят фантастическими, но, безусловно, перспективными для будущих разработок.

AIM: to study modern approaches to the application of machine learning and deep learning technologies for the management of patients with colorectal cancer.MATERIALS AND METHODS: after screening 398 publications, 112 articles were selected and the full text of the works was studied. After studying the full texts of the articles, the works were selected, machine learning models in which showed an accuracy of more than 80%. The results of 41 original publications were used to write this review.RESULTS: several areas have been identified that are the most promising for the use of artificial intelligence technologies in the management of patients with colorectal cancer. They are predicting the response to neoadjuvant treatment, predicting the risks of metastasis and recurrence of the disease, predicting the toxicity of chemotherapy, assessing the risks of leakage of colorectal anastomoses. As the most promising factors that can be used to train models, researchers consider clinical parameters, the immune environment of the tumor, tumor RNA signatures, as well as visual pathomorphological characteristics. The models for predicting the risk of liver metastases in patients with stage T1 (AUC = 0.9631), as well as models aimed at assessing the risk of 30-day mortality during chemotherapy (AUC = 0.924), were characterized with the greatest accuracy. Most of the technologies discussed in this paper are software products trained on data sets of different quality and quantity, which are able to suggest a treatment scenario based on predictive models, and, in fact, can be used as a doctor’s assistant with very limited functionality.CONCLUSION: the current level of digital technologies in oncology and in the treatment of colorectal cancer does not allow us to talk about a strong AI capable of making decisions about the treatment of patients without medical supervision. Personalized treatment based on the microbiotic and mutation spectrum and, for example, personal pharmacokinetics, so far look fantastic, but certainly promising for future developments.

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