Using deep learning to improve nonsystematic viewing of breast cancer on MRI Journal Article


Authors: Eskreis-Winkler, S.; Onishi, N.; Pinker, K.; Reiner, J. S.; Kaplan, J.; Morris, E. A.; Sutton, E. J.
Article Title: Using deep learning to improve nonsystematic viewing of breast cancer on MRI
Abstract: Objective: To investigate the feasibility of using deep learning to identify tumor-containing axial slices on breast MRI images. Methods: This IRB-approved retrospective study included consecutive patients with operable invasive breast cancer undergoing pretreatment breast MRI between January 1, 2014, and December 31, 2017. Axial tumor-containing slices from the first postcontrast phase were extracted. Each axial image was subdivided into two subimages: one of the ipsilateral cancer-containing breast and one of the contralateral healthy breast. Cases were randomly divided into training, validation, and testing sets. A convolutional neural network was trained to classify subimages into "cancer"and "no cancer"categories. Accuracy, sensitivity, and specificity of the classification system were determined using pathology as the reference standard. A two-reader study was performed to measure the time savings of the deep learning algorithm using descriptive statistics. Results: Two hundred and seventy-three patients with unilateral breast cancer met study criteria. On the held-out test set, accuracy of the deep learning system for tumor detection was 92.8% (648/706; 95% confidence interval: 89.7%-93.8%). Sensitivity and specificity were 89.5% and 94.3%, respectively. Readers spent 3 to 45 seconds to scroll to the tumor-containing slices without use of the deep learning algorithm. Conclusion: In breast MR exams containing breast cancer, deep learning can be used to identify the tumor-containing slices. This technology may be integrated into the picture archiving and communication system to bypass scrolling when viewing stacked images, which can be helpful during nonsystematic image viewing, such as during interdisciplinary tumor board meetings. © 2021 Society of Breast Imaging 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Keywords: breast cancer; artificial intelligence; breast mri; tumor detection; deep learning; convolutional neural network
Journal Title: Journal of Breast Imaging
Volume: 3
Issue: 2
ISSN: 2631-6110
Publisher: Oxford University Press  
Date Published: 2021-03-01
Start Page: 201
End Page: 207
Language: English
DOI: 10.1093/jbi/wbaa102
PROVIDER: scopus
DOI/URL:
Notes: Article -- Export Date: 1 June 2021 -- Source: Scopus
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