Review Article
Artificial intelligence in the interpretation of breast cancer on MRI
Deepa Sheth MD,
Maryellen L. Giger PhD,
Corresponding Author
Deepa Sheth MD
Department of Radiology, University of Chicago, Chicago, Illinois, USA
Address reprint requests to: D.S., Department of Radiology, University of Chicago, 5841 S Maryland Ave., Rm. P221, MC 2026, Chicago, IL 60637. E-mail: [email protected]Search for more papers by this authorMaryellen L. Giger PhD
Department of Radiology, University of Chicago, Chicago, Illinois, USA
Search for more papers by this authorDeepa Sheth MD,
Maryellen L. Giger PhD,
Corresponding Author
Deepa Sheth MD
Department of Radiology, University of Chicago, Chicago, Illinois, USA
Address reprint requests to: D.S., Department of Radiology, University of Chicago, 5841 S Maryland Ave., Rm. P221, MC 2026, Chicago, IL 60637. E-mail: [email protected]Search for more papers by this authorMaryellen L. Giger PhD
Department of Radiology, University of Chicago, Chicago, Illinois, USA
Search for more papers by this authorAbstract
Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer-aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image-specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI-based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient-specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer.
Level of Evidence: 3
Technical Efficacy: Stage 3
J. Magn. Reson. Imaging 2020;51:1310–1324.
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