AI applications to breast MRI: Today and tomorrow Review

   


Authors: Lo Gullo, R.; Brunekreef, J.; Marcus, E.; Han, L. K.; Eskreis-Winkler, S.; Thakur, S. B.; Mann, R.; Groot Lipman, K.; Teuwen, J.; Pinker, K.
Review Title: AI applications to breast MRI: Today and tomorrow
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. © 2024 International Society for Magnetic Resonance in Medicine.
Keywords: treatment outcome; treatment response; review; area under the curve; nuclear magnetic resonance imaging; positron emission tomography; magnetic resonance imaging; quality control; breast cancer; image analysis; image interpretation, computer-assisted; breast; diagnostic imaging; breast neoplasms; prediction; risk assessment; mammography; computer assisted diagnosis; image quality; artificial intelligence; breast tumor; dynamic contrast-enhanced magnetic resonance imaging; diffusion weighted imaging; predictive value; image reconstruction; breast lesion; breast magnetic resonance imaging; procedures; machine learning; support vector machine; workflow; breast imaging reporting and data system; humans; human; female; deep learning; convolutional neural network; digital breast tomosynthesis; t1 weighted imaging
Journal Title: Journal of Magnetic Resonance Imaging
Volume: 60
Issue: 6
ISSN: 1053-1807
Publisher: Wiley Blackwell  
Date Published: 2024-12-01
Start Page: 2290
End Page: 2308
Language: English
DOI: 10.1002/jmri.29358
PUBMED: 38581127
PROVIDER: scopus
PMCID: PMC11452568
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189957687&doi=10.1002%2fjmri.29358&partnerID=40&md5=fb6dee55c025499b73631817dd83e5b3
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding MSK author is Katja Pinker-- Source: Scopus
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MSK Authors
  1. Sunitha Bai Thakur
    100 Thakur
  2. Katja Pinker-Domenig
    213 Pinker-Domenig
  3. Sarah Eskreis-Winkler
    26 Eskreis-Winkler
  4. Roberto Lo Gullo
    33 Lo Gullo
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