Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer.
Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer.
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基于组织学的人工智能检测微卫星不稳定性和对结直肠癌免疫疗法反应的预测。
DOI：
10.3390/cancers13030391
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发表时间：
2021-01-21
期刊：
影响因子：
5.2
通讯作者：
Maoz A
中科院分区：
文献类型：
--
作者：
Hildebrand LA;Pierce CJ;Dennis M;Paracha M;Maoz A
称为错配修复（MMR）的DNA修复途径中的缺陷可导致癌症，包括结直肠癌（CRC）。错配修复缺陷（dMMR）的检测是基于分子测试，其中之一是微卫星不稳定性（MSI）测试。用dMMR/MSI检测肿瘤对于鉴别Lynch综合征患者和确定患者是否可以从免疫治疗中获益非常重要。最近，人工智能已被评估为直接从大多数癌症患者可用的组织切片预测MSI/dMMR的方法。我们回顾了关于机器学习在dMMR/MSI分类中的应用的数据，包括其准确性和局限性，重点是CRC。我们还提供了以前的努力，以预测MSI从组织切片和背景有关使用人工智能的图像分析的概述。我们总结了最近使用人工智能预测MSI的努力，并讨论了预测免疫治疗反应的影响。微卫星不稳定性（MSI）是DNA错配修复缺陷（dMMR）的分子标志物，在约15%的结直肠癌（CRC）患者中发现。建议对所有CRC患者进行MSI/dMMR检测，以筛查Lynch综合征，最近，以确定晚期疾病中免疫检查点抑制剂的资格。然而，由于成本和资源的限制，MSI/dMMR的通用测试尚未统一实施。人工智能已用于直接从苏木精和伊红（H&E）染色的组织载玻片预测MSI/dMMR。我们回顾了关于机器学习用于MSI分类的实用性的新兴数据，重点是CRC。我们还为临床医生介绍了机器学习和卷积神经网络的图像分析。机器学习可以在高质量的策划数据集中以高精度预测MSI/dMMR。当应用于具有不同种族和/或临床特征或不同组织制备方案的队列时，准确度可能显著降低。研究正在进行中，以确定预测MSI的最佳机器学习方法，这需要与当前的临床实践进行比较，包括下一代测序。预测对免疫疗法的反应仍然是一个未满足的需求。
Defects in a DNA repair pathway called mismatch repair (MMR) can lead to cancer, including colorectal cancer (CRC). The detection of mismatch repair deficiency (dMMR) is based on molecular tests, one of which is microsatellite instability (MSI) testing. Detecting tumors with dMMR/MSI is important for the identification of patients with Lynch Syndrome and determining if patients may benefit from immunotherapy. Recently, artificial intelligence has been evaluated as a method to predict MSI/dMMR directly from tissue slides that are available for most cancer patients. We review the data regarding the utility of machine learning for dMMR/MSI classification, including its accuracy and limitations, focusing on CRC. We also provide an overview of previous efforts to predict MSI from tissue slides and background regarding the use of artificial intelligence for image analyses. We summarize recent efforts to use artificial intelligence for the prediction of MSI and discuss the implications for predicting response to immunotherapy. Microsatellite instability (MSI) is a molecular marker of deficient DNA mismatch repair (dMMR) that is found in approximately 15% of colorectal cancer (CRC) patients. Testing all CRC patients for MSI/dMMR is recommended as screening for Lynch Syndrome and, more recently, to determine eligibility for immune checkpoint inhibitors in advanced disease. However, universal testing for MSI/dMMR has not been uniformly implemented because of cost and resource limitations. Artificial intelligence has been used to predict MSI/dMMR directly from hematoxylin and eosin (H&E) stained tissue slides. We review the emerging data regarding the utility of machine learning for MSI classification, focusing on CRC. We also provide the clinician with an introduction to image analysis with machine learning and convolutional neural networks. Machine learning can predict MSI/dMMR with high accuracy in high quality, curated datasets. Accuracy can be significantly decreased when applied to cohorts with different ethnic and/or clinical characteristics, or different tissue preparation protocols. Research is ongoing to determine the optimal machine learning methods for predicting MSI, which will need to be compared to current clinical practices, including next-generation sequencing. Predicting response to immunotherapy remains an unmet need.
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影响因子：
4.6
作者：
Bychkov D;Linder N;Turkki R;Nordling S;Kovanen PE;Verrill C;Walliander M;Lundin M;Haglund C;Lundin J
通讯作者：
Lundin J
影响因子：
4
作者：
Fujiyoshi K;Yamaguchi T;Kakuta M;Takahashi A;Arai Y;Yamada M;Yamamoto G;Ohde S;Takao M;Horiguchi SI;Natsume S;Kazama S;Nishizawa Y;Nishimura Y;Akagi Y;Sakamoto H;Akagi K
通讯作者：
Akagi K
影响因子：
45.3
作者：
Bessa, Xavier;Alenda, Cristina;Andreu, Montserrat
通讯作者：
Andreu, Montserrat
影响因子：
78.8
作者：
Bera, Kaustav;Schalper, Kurt A.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant
影响因子：
7.3
作者：
Briggs, Sarah;Tomlinson, Ian
通讯作者：
Tomlinson, Ian