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Clinical Studies

Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer

British Journal of Cancer volume 128, pages 2141–2149 (2023)Cite this article

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Abstract

Triple-negative breast cancer (TNBC) accounts for 15–20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.

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Fig. 1: Branches of artificial intelligence technology and the relationship between AI and ML and DL, with examples of commonly used algorithms.
Fig. 2: A framework of AI in TNBC.
Fig. 3: A patterned pipeline for TNBC subtyping.

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Funding

This research was supported by the Key Research and Development Project of Sichuan Province (2023YFS0171 to JM).

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Author notes

  1. These authors contributed equally: Jiamin Guo, Junjie Hu.

Authors and Affiliations

  1. Department of Medical Oncology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China

    Jiamin Guo, Yichen Zheng & Ji Ma

  2. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan Province, P. R. China

    Junjie Hu

  3. Department of Radiology, West China Hospital of Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China

    Shuang Zhao

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  1. Jiamin Guo
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JM and SZ designed and prepared the manuscript. JG, JH, YZ and JM drafted the manuscript.

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Correspondence to Shuang Zhao or Ji Ma.

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Guo, J., Hu, J., Zheng, Y. et al. Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer. Br J Cancer 128, 2141–2149 (2023). https://doi.org/10.1038/s41416-023-02215-z

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