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Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic reviewстатья
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 1 мая 2024 г.
- Авторы: Lobanova Olga Andreevna, Kolesnikova Anastasia Olegovna, Ponomareva Valeria Aleksandrovna, Vekhova Ksenia Andreevna, Shaginyan Anaida Lusparonovna, Semenova Alisa Borisovna, Nekhoroshkov Dmitry Petrovich, Kochetkova Svetlana Evgenievna, Kretova Natalia Valeryevna, Zanozin Alexander Sergeevich, Peshkova Maria Alekseevna, Serezhnikova Natalia Borisovna, Zharkov Nikolay Vladimirovich, Kogan Evgeniya Altarovna, Biryukov Alexander Alekseevich, Rudenko Ekaterina Evgenievna, Demura Tatiana Alexandrovna
- Журнал: Journal of pathology informatics
- Том: 15
- Год издания: 2024
- Издательство: Association for Pathology Informatics
- Местоположение издательства: Pittsburgh, PA, United States
- Номер статьи: 100353
- DOI: 10.1016/j.jpi.2023.100353
- Аннотация: Evaluation of the parameters such as tumor microenvironment (TME) and tumor budding (TB) is one of the most important steps in colorectal cancer (CRC) diagnosis and cancer development prognosis. In recent years, artificial intelligence (AI) has been successfully used to solve such problems. In this paper, we summarize the latest data on the use of artificial intelligence to predict tumor microenvironment and tumor budding in histological scans of patients with colorectal cancer. We performed a systematic literature search using 2 databases (Medline and Scopus) with the following search terms: ("tumor microenvironment" OR "tumor budding") AND ("colorectal cancer" OR CRC) AND ("artificial intelligence" OR "machine learning " OR "deep learning"). During the analysis, we gathered from the articles performance scores such as sensitivity, specificity, and accuracy of identifying TME and TB using artificial intelligence. The systematic review showed that machine learning and deep learning successfully cope with the prediction of these parameters. The highest accuracy values in TB and TME prediction were 97.7% and 97.3%, respectively. This review led us to the conclusion that AI platforms can already be used as diagnostic aids, which will greatly facilitate the work of pathologists in detection and estimation of TB and TME as instruments and second-opinion services. A key limitation in writing this systematic review was the heterogeneous use of performance metrics for machine learning models by different authors, as well as relatively small datasets used in some studies.
- Добавил в систему: Вехова Ксения Андреевна