Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review

• Principais autores: Olga Andreevna Lobanova (Autor), Anastasia Olegovna Kolesnikova (Autor), Valeria Aleksandrovna Ponomareva (Autor), Ksenia Andreevna Vekhova (Autor), Anaida Lusparonovna Shaginyan (Autor), Alisa Borisovna Semenova (Autor), Dmitry Petrovich Nekhoroshkov (Autor), Svetlana Evgenievna Kochetkova (Autor), Natalia Valeryevna Kretova (Autor), Alexander Sergeevich Zanozin (Autor), Maria Alekseevna Peshkova (Autor), Natalia Borisovna Serezhnikova (Autor), Nikolay Vladimirovich Zharkov (Autor), Evgeniya Altarovna Kogan (Autor), Alexander Alekseevich Biryukov (Autor), Ekaterina Evgenievna Rudenko (Autor), Tatiana Alexandrovna Demura (Autor)
• Formato: Livro
• Publicado em: Elsevier, 2024-12-01T00:00:00Z.
• Assuntos: article
• Acesso em linha: Connect to this object online.
• Notes: 2153-3539; 10.1016/j.jpi.2023.100353
• Keywords: Colorectal cancer; Systematic review; Tumor microenvironment; Tumor budding; Artificial intelligence; Computer applications to medicine. Medical informatics; R858-859.7; Pathology; RB1-214
• Source: Journal of Pathology Informatics, Vol 15, Iss , Pp 100353- (2024)

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.