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将人工智能应用于癌症免疫治疗。

Applying artificial intelligence for cancer immunotherapy.

作者信息

Xu Zhijie, Wang Xiang, Zeng Shuangshuang, Ren Xinxin, Yan Yuanliang, Gong Zhicheng

机构信息

Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China.

Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China.

出版信息

Acta Pharm Sin B. 2021 Nov;11(11):3393-3405. doi: 10.1016/j.apsb.2021.02.007. Epub 2021 Feb 11.


DOI:10.1016/j.apsb.2021.02.007
PMID:34900525
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8642413/
Abstract

Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.

摘要

人工智能(AI)是一个通用术语,指利用机器模仿智能行为,以最少的人工干预执行复杂任务,如机器学习;这项技术正在彻底改变和重塑医学。人工智能在完善医疗保健系统方面具有巨大潜力,涵盖诊断、风险分析、健康信息管理、生活方式监督和虚拟健康辅助等领域。在免疫治疗方面,人工智能已被应用于基于免疫特征、医学成像和组织学分析来预测免疫治疗反应。鉴于这些技术在提高诊断准确性、优化治疗方案、预测护理结果和降低人力资源成本方面的性能不断提升,它们在癌症免疫治疗管理中也可能非常有用。在本综述中,我们阐述了人工智能的细节以及目前将人工智能应用于癌症免疫治疗的进展和技术现状。此外,我们讨论了将该技术广泛应用于临床所面临的挑战、机遇和相应策略。最后,我们总结了人工智能对癌症免疫治疗的影响,并对人工智能未来的潜在应用提供了我们的观点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/b58341195b7c/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/118442965d7d/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/ea69321667e0/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/f0554fab658b/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/b58341195b7c/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/118442965d7d/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/ea69321667e0/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/f0554fab658b/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5cc/8642413/b58341195b7c/gr3.jpg

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本文引用的文献

[1]
Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence.

Int J Mol Sci. 2020-4-19

[2]
Fellow in a Box: Combining AI and Domain Knowledge with Bayesian Networks for Differential Diagnosis in Neuroimaging.

Radiology. 2020-6

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BMJ. 2020-3-25

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Invest Radiol. 2020-9

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J Hematol Oncol. 2019-12-4

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Front Genet. 2019-11-19

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J Hematol Oncol. 2019-11-28

[9]
"Electronic Nose" Predicts Immunotherapy Response.

JAMA. 2019-11-12

[10]
Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

Radiology. 2019-10-1

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