Abstract
Objectives
Ultrasound examination has inter-observer and intra-observer variability and a high
false-positive rate. The aim of this study was to evaluate the value of the combined
use of a deep learning-based computer-aided diagnosis (CAD) system and ultrasound
elastography with conventional ultrasound (US) in increasing specificity and reducing
unnecessary breast lesions biopsies.
Materials and Methods
Conventional US, CAD system, and strain elastography (SE) were retrospectively performed
on 216 breast lesions before biopsy or surgery. The area under the receiver operating
characteristic curve (AUC), sensitivity, specificity, and biopsy rate were compared
between conventional US and the combination of conventional US, SE, and CAD system.
Results
Of 216 lesions, 54 were malignant and 162 were benign. The addition of CAD system
and SE to conventional US increased the AUC from 0.716 to 0.910 and specificity from
46.9% to 85.8% without a loss in sensitivity while 89.2% (66 of 74) of benign lesions
in Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions would avoid
unnecessary biopsies.
Conclusion
The addition of CAD system and SE to conventional US improved specificity and AUC
without loss of sensitivity, and reduced unnecessary biopsies.
Keywords
Abbreviations:
US (ultrasound), BI-RADS (Breast Imaging Reporting and Data System), CAD (computer-aided diagnosis), UE (ultrasound elastography), SE (strain elastography), ROI (region of interest), AUC (area under the receiver operating characteristic curve), IDC (invasive ductal carcinoma), DCIS (ductal carcinoma in situ), ILC (invasive lobular carcinoma), ROC (receiver operating characteristic)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: December 22, 2022
Accepted:
December 20,
2022
Received in revised form:
November 27,
2022
Received:
September 18,
2022
Identification
Copyright
© 2022 Published by Elsevier Inc.
