Machine learning in breast MRI

被引:111
作者
Reig, Beatriu [1 ]
Heacock, Laura [2 ]
Geras, Krzysztof J. [2 ]
Moy, Linda [2 ,3 ]
机构
[1] NYU, Dept Radiol, Sch Med, 560 1St Ave, New York, NY 10016 USA
[2] NYU, Dept Radiol, Sch Med, Bernard & Irene Schwartz Ctr Biomed Imaging, 560 1St Ave, New York, NY 10016 USA
[3] NYU, Sch Med, Ctr Adv Imaging Innovat & Res CAI2 R, New York, NY USA
基金
美国国家卫生研究院;
关键词
breast; MR; machine learning; deep learning; artificial intelligence; radiomics; BACKGROUND PARENCHYMAL ENHANCEMENT; CARCINOMA IN-SITU; CANCER MOLECULAR SUBTYPE; RECURRENCE-FREE SURVIVAL; SUPPORT VECTOR MACHINE; DCE-MRI; NEOADJUVANT CHEMOTHERAPY; PREOPERATIVE PREDICTION; FIBROGLANDULAR TISSUE; MAMMOGRAPHIC DENSITY;
D O I
10.1002/jmri.26852
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Technical Efficacy Stage:2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.
引用
收藏
页码:998 / 1018
页数:21
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