AI Applications to Breast MRI: Today and Tomorrow.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date:

Abstract

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Authors

  • Roberto Lo Gullo
    Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.
  • Joren Brunekreef
    Netherlands Cancer Institute, Amsterdam, The Netherlands. j.brunekreef@nki.nl.
  • Eric Marcus
    AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands. Electronic address: e.marcus@nki.nl.
  • Lynn K Han
    Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA.
  • Sarah Eskreis-Winkler
    Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.
  • Sunitha B Thakur
  • Ritse Mann
    Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Kevin Groot Lipman
    AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Jonas Teuwen
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Katja Pinker
    Department of Radiology, Columbia University, Vagelos College of Physicians and Surgeons, New York, New York, USA.