Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review
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...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353923001670 |
| _version_ | 1797375151089647616 |
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| author | Olga Andreevna Lobanova Anastasia Olegovna Kolesnikova Valeria Aleksandrovna Ponomareva Ksenia Andreevna Vekhova Anaida Lusparonovna Shaginyan Alisa Borisovna Semenova Dmitry Petrovich Nekhoroshkov Svetlana Evgenievna Kochetkova Natalia Valeryevna Kretova Alexander Sergeevich Zanozin Maria Alekseevna Peshkova Natalia Borisovna Serezhnikova Nikolay Vladimirovich Zharkov Evgeniya Altarovna Kogan Alexander Alekseevich Biryukov Ekaterina Evgenievna Rudenko Tatiana Alexandrovna Demura |
| author_facet | Olga Andreevna Lobanova Anastasia Olegovna Kolesnikova Valeria Aleksandrovna Ponomareva Ksenia Andreevna Vekhova Anaida Lusparonovna Shaginyan Alisa Borisovna Semenova Dmitry Petrovich Nekhoroshkov Svetlana Evgenievna Kochetkova Natalia Valeryevna Kretova Alexander Sergeevich Zanozin Maria Alekseevna Peshkova Natalia Borisovna Serezhnikova Nikolay Vladimirovich Zharkov Evgeniya Altarovna Kogan Alexander Alekseevich Biryukov Ekaterina Evgenievna Rudenko Tatiana Alexandrovna Demura |
| author_sort | Olga Andreevna Lobanova |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T19:18:06Z |
| format | Article |
| id | doaj.art-f7c8ab7a3c294cefb5801a78a0a569d0 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-03-08T19:18:06Z |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-f7c8ab7a3c294cefb5801a78a0a569d02023-12-27T05:25:41ZengElsevierJournal of Pathology Informatics2153-35392024-12-0115100353Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic reviewOlga Andreevna Lobanova0Anastasia Olegovna Kolesnikova1Valeria Aleksandrovna Ponomareva2Ksenia Andreevna Vekhova3Anaida Lusparonovna Shaginyan4Alisa Borisovna Semenova5Dmitry Petrovich Nekhoroshkov6Svetlana Evgenievna Kochetkova7Natalia Valeryevna Kretova8Alexander Sergeevich Zanozin9Maria Alekseevna Peshkova10Natalia Borisovna Serezhnikova11Nikolay Vladimirovich Zharkov12Evgeniya Altarovna Kogan13Alexander Alekseevich Biryukov14Ekaterina Evgenievna Rudenko15Tatiana Alexandrovna Demura16I.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; Corresponding author.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, RussiaLLC “Intelligent analytics”, Moscow, RussiaI.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, RussiaI.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, RussiaI.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, RussiaLLC “Intelligent analytics”, Moscow, RussiaI.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, RussiaI.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, RussiaI.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, RussiaI.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, RussiaI.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, RussiaI.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, RussiaI.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, RussiaI.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, RussiaI.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, RussiaI.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, RussiaEvaluation 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.http://www.sciencedirect.com/science/article/pii/S2153353923001670Colorectal cancerSystematic reviewTumor microenvironmentTumor buddingArtificial intelligence |
| spellingShingle | Olga Andreevna Lobanova Anastasia Olegovna Kolesnikova Valeria Aleksandrovna Ponomareva Ksenia Andreevna Vekhova Anaida Lusparonovna Shaginyan Alisa Borisovna Semenova Dmitry Petrovich Nekhoroshkov Svetlana Evgenievna Kochetkova Natalia Valeryevna Kretova Alexander Sergeevich Zanozin Maria Alekseevna Peshkova Natalia Borisovna Serezhnikova Nikolay Vladimirovich Zharkov Evgeniya Altarovna Kogan Alexander Alekseevich Biryukov Ekaterina Evgenievna Rudenko Tatiana Alexandrovna Demura Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review Journal of Pathology Informatics Colorectal cancer Systematic review Tumor microenvironment Tumor budding Artificial intelligence |
| title | Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review |
| title_full | Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review |
| title_fullStr | Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review |
| title_full_unstemmed | Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review |
| title_short | Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review |
| title_sort | artificial intelligence ai for tumor microenvironment tme and tumor budding tb identification in colorectal cancer crc patients a systematic review |
| topic | Colorectal cancer Systematic review Tumor microenvironment Tumor budding Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2153353923001670 |
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