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Radiomics and artificial intelligence in breast imaging: a survey

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Abstract

Medical imaging techniques, such as mammography, ultrasound and magnetic resonance imaging, plays an integral role in the detection and characterization of breast cancer. Although computers are believed to gain an important role in the assessment of medical images for breast evaluation for at least two decades, their impact on performance has not lived up to expectations yet. With the continuous and rapid development of computer science, artificial intelligence (AI) approaches, like machine learning and deep learning, have been introduced for the analysis of medical images. Because of the remarkable advances in data extraction and analysis in medical imaging compared to conventional feature-based techniques, AI has reignited the interest in automated breast image interpretation. Extensive research is conducted on accurate detection and classification of breast lesions, and more specifically, the predictive and prognostic features of breast cancer by radiomics. Radiomics exploits the fact that image data is nowadays numerical and can also be used to generate quantitative biomarkers. In this comprehensive review, we cover the progress, application and challenge of radiomics and AI in breast cancer diagnosis in recent years, as well as the impact and significance of AI on future breast cancer research.

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Acknowledgements

The authors thank to the support from the Oversea Study Program of Guangzhou Elite Project.

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No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, Amsterdam, 1066 CX, The Netherlands

    Tianyu Zhang, Tao Tan, Yuan Gao, Xin Wang, Luyi Han, Regina G. H. Beets-Tan & Ritse M. Mann

  2. GROW School for Oncology and Development Biology, Maastricht University, P. O. Box 616, Maastricht, 6200 MD, The Netherlands

    Tianyu Zhang, Yuan Gao, Xin Wang & Regina G. H. Beets-Tan

  3. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, Nijmegen, 6525 GA, The Netherlands

    Tianyu Zhang, Riccardo Samperna, Yuan Gao, Xin Wang, Luyi Han & Ritse M. Mann

  4. Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao, China

    Tao Tan

  5. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China

    Zhang Li & Qifeng Yu

  6. Hunan Key Laboratory for Image Measurement and Vision Navigation, Changsha, Hunan, China

    Zhang Li & Qifeng Yu

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Contributions

T. Z., R. S., Y. G., X. W., and L. H. collected the articles; T. Z., T. T., R. S., Y. G., X. W., L. H., Q. Y. and R. M. M. analyzed the articles; R. M. M. and R. G. H. B. T. provided project administration and resources; T. Z. wrote the main manuscript text, T. T. and R. M. M. revised it. All authors approved the final version of this article.

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Correspondence to Tao Tan.

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Zhang, T., Tan, T., Samperna, R. et al. Radiomics and artificial intelligence in breast imaging: a survey. Artif Intell Rev 56 (Suppl 1), 857–892 (2023). https://doi.org/10.1007/s10462-023-10543-y

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