Journal
JOURNAL OF MAGNETIC RESONANCE IMAGING
Volume 51, Issue 5, Pages 1310-1324Publisher
WILEY
DOI: 10.1002/jmri.26878
Keywords
artificial intelligence; MRI; breast imaging; computer-aided diagnosis; machine learning; deep learning; radiomics
Funding
- National Institutes of Health (NIH) [CA195564]
Ask authors/readers for more resources
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. Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available