Artificial intelligence in the interpretation of breast cancer on MRI. Issue 5 (25th July 2019)

• Record Type: Journal Article
• Authors: Sheth, Deepa; Giger, Maryellen L.
• Is Part Of: Journal of magnetic resonance imaging. Volume 51:Issue 5(2020)
• Page Start: 1310
• Page End: 1324
• Publication Date: 2019-07-25
• Subjects: artificial intelligence -- MRI -- breast imaging -- computer‐aided diagnosis -- machine learning -- deep learning -- radiomics; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26878
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : 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.