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
I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Trubetskaya street, 8, p. 2, 119991 Moscow, Russia.
LLC "Intelligent analytics", Moscow, Russia.
J Pathol Inform. 2023 Nov 22;15:100353. doi: 10.1016/j.jpi.2023.100353. eCollection 2024 Dec.
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.
评估肿瘤微环境（TME）和肿瘤芽生（TB）等参数是结直肠癌（CRC）诊断和癌症发展预后中最重要的步骤之一。近年来，人工智能（AI）已成功用于解决此类问题。在本文中，我们总结了关于使用人工智能预测结直肠癌患者组织学扫描中的肿瘤微环境和肿瘤芽生的最新数据。我们使用2个数据库（Medline和Scopus）进行了系统的文献检索，检索词如下：（“肿瘤微环境”或“肿瘤芽生”）与（“结直肠癌”或CRC）以及（“人工智能”或“机器学习”或“深度学习”）。在分析过程中，我们从文章中收集了使用人工智能识别TME和TB的性能得分，如敏感性、特异性和准确性。系统评价表明，机器学习和深度学习成功地应对了这些参数的预测。TB和TME预测中的最高准确率分别为97.7%和97.3%。这篇综述使我们得出结论，人工智能平台已经可以用作诊断辅助工具，这将极大地便利病理学家在检测和评估TB和TME方面的工作，作为工具和第二意见服务。撰写本系统评价的一个关键限制是不同作者对机器学习模型性能指标的异质性使用，以及一些研究中使用的数据集相对较小。
