AI-enhanced diagnosis of challenging lesions in breast MRI: A methodology and application primer Journal Article


Authors: Meyer-Base, A.; Morra, L.; Tahmassebi, A.; Lobbes, M.; Meyer-Base, U.; Pinker, K.
Article Title: AI-enhanced diagnosis of challenging lesions in breast MRI: A methodology and application primer
Abstract: Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine-learning (ML) techniques. In this review article, we describe applications of ML-based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state-of-the-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast. Level of Evidence: 2. Technical Efficacy Stage: 2. © 2020 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC. on behalf of International Society for Magnetic Resonance in Medicine.
Keywords: review; nuclear magnetic resonance imaging; magnetic resonance imaging; breast cancer; prediction; computer assisted diagnosis; contrast enhancement; artificial intelligence; breast tumor; clinical effectiveness; neoadjuvant chemotherapy; breast lesion; image segmentation; fuzzy system; decision tree; machine learning; support vector machine; morphologic features; multiparametric magnetic resonance imaging; human; random forest; deep learning; radiomics; kinetic features; bayesian network; k nearest neighbor; computer-aided diagnosis systems
Journal Title: Journal of Magnetic Resonance Imaging
Volume: 54
Issue: 3
ISSN: 1053-1807
Publisher: Wiley Blackwell  
Date Published: 2021-09-01
Start Page: 686
End Page: 702
Language: English
DOI: 10.1002/jmri.27332
PUBMED: 32864782
PROVIDER: scopus
PMCID: PMC8451829
DOI/URL:
Notes: Review -- Export Date: 1 September 2021 -- Source: Scopus
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