Rationale and Objectives
To propose a novel deep learning method incorporating multiple regions based on contrast-enhanced
ultrasound and grayscale ultrasound, evaluate its performance in reducing false positives
for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions, and compare
its diagnostic performance with that of ultrasound experts.
Materials and Methods
This study enrolled 163 breast lesions in 161 women from November 2018 to March 2021.
Contrast-enhanced ultrasound and conventional ultrasound were performed before surgery
or biopsy. A novel deep learning model incorporating multiple regions based on contrast-enhanced
ultrasound and grayscale ultrasound was proposed for minimizing the number of false-positive
biopsies. The area under the receiver operating characteristic curve (AUC), sensitivity,
specificity, and accuracy were compared between the deep learning model and ultrasound
experts.
Results
The AUC, sensitivity, specificity, and accuracy of the deep learning model in BI-RADS
category 4 lesions were 0.910, 91.5%, 90.5%, and 90.8%, respectively, compared with
those of ultrasound experts were 0.869, 89.4%, 84.5%, and 85.9%, respectively.
Conclusion
The novel deep learning model we proposed had a diagnostic accuracy comparable to
that of ultrasound experts, showing the potential to be clinically useful in minimizing
the number of false-positive biopsies.
Key Words
Abbreviations:
AUC (area under the receiver operating characteristic curve), BI-RADS (Breast Imaging Reporting and Data System), CEUS (contrast-enhanced ultrasound), CI (confidence interval), GLCM (gray-level co-occurrence matrix), GUS (grayscale ultrasound), ICC (intraclass correlation coefficient), PGBM (point-wise gated Boltzmann machine), PGDN (point-wise gated deep network), RBM (restricted Boltzmann machine), ROI (region of interest), SD (standard deviation), SVM (support vector machine), US (ultrasound)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: April 22, 2023
Accepted:
March 1,
2023
Received in revised form:
March 1,
2023
Received:
October 28,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
