vitjВрач и информационные технологииMedical Doctor and Information Technologies1811-01932413-5208Pirogov National Medical and Surgical Center10.25881/18110193_2022_2_22vitj-125Research ArticleОБЗОРЫREVIEWSАналитический обзор технологий моделирования сценариев скрининга рака молочной железы.Analytical review of technologies for simulation of breast cancer screening scenarioЗавьяловА. А.ZavyalovA. A.

д.м.н., профессор

г. Москва

Dr. Sci. (Medicine), professor

Moscow

AZAV06@mail.ruАндреевД. А.AndreevD. A.

д.м.н.

г. Москва

Dr. Sci. (Medicine)

Moscow

AndreevDA@zdrav.mos.ruГБУ «Научно-исследовательский институт организации здравоохранения и медицинского менеджмента ДЗМ»РоссияResearch Institute for Healthcare Organization and Medical Management of Moscow Health Department,Russian Federation202230032025022233Copyright © Завьялов А.А., Андреев Д.А., 20252025Завьялов А.А., Андреев Д.А.Zavyalov A.A., Andreev D.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.vit-j.ru/jour/article/view/125Актуальность

Актуальность. Конечные эффекты скрининга рака крайне трудно изучить путем проведения рандомизированных контролируемых клинических исследований в реальной практике. Растет роль предиктивного моделирования в онкологии. Моделирование последствий интервенционных вмешательств в онкологии основано, в том числе на применении наборов инструментов, обозначаемых термином «математическая онкология».

Цель

Цель. Исследование подходов к моделированию сценариев скрининга рака молочной железы (РМЖ), направленных на разработку инструментов поддержки принятия врачебных решений в системе здравоохранения, включая выработку клинических рекомендаций по проведению онкоскрининга.

Материал и методы

Материал и методы. Для поиска релевантных сведений применялись база данных PubMed (Medline) и система GOOGLE. В поисковой строке вводились запросы по теме моделирования программ скрининга РМЖ. Использовались такие термины, как: «breast cancer», «screening», «modeling», «oncology informatics», «cancer care», «big data» и прочие. Результаты. Рассмотрены примеры моделей скрининга РМЖ. Результаты моделирования могут включать полный спектр клинически и экономически значимых параметров, характеризующих анализируемые сценарии скрининга. Изучены базовые концепции построения валидных моделей, включающие анализ и имитацию индивидуальных историй течения опухолевого процесса (естественных и в условиях интервенционного вмешательства).

Выводы

Выводы. Имитационное моделирование позволяет установить связь между новыми достижениями в исследованиях злокачественных новообразований и наиболее эффективными стратегиями их внедрения в клиническую практику с целью получения максимальной пользы для пациента и снижения экономической нагрузки на популяционном уровне.

Background

Background. Long-term outcomes of screening programs are challenging to evaluate in randomized clinical trials. The role of predictive modeling is becoming increasingly popular in oncology. Modeling the interventions consequences in oncology is based, among other things, on the use of toolkits, denoted by the term «mathematical oncology»

Aim

Aim. To study approaches to modeling screening scenarios for breast cancer, aimed at developing tools to support medical decision-making in the healthcare system, including the development of clinical guidelines for cancer screening.

Methods

Methods. The search for relevant studies was performed through PubMed (Medline) and direct google-search. Key words for the search included breast cancer», «screening», «modeling», «oncology informatics», «cancer care», «big data» etc.

Results

Results. We analyzed several breast cancer screening models. Results of the modeling included broad spectrum of clinically and economically parameters relevant for the screening scenarios characterization. The basic concepts of constructing valid models, including the analysis and simulation of individual histories of the tumor progression course (both natural and in interventional settings), were studied.

Conclusion

Conclusion. Simulation modeling allowed linking new advances in cancer research with the most effective strategies for implementing them into clinical practice in order to maximize patient benefit and reduce economic burden at the population level.

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