• Min Young Kim, Soo-Yeon Kim, Yeon Soo Kim, Eun Sil Kim, and Jung Min Chang.
• Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
• Medicine (Baltimore). 2021 Aug 6; 100 (31): e26823e26823.

AbstractLow specificity and operator dependency are the main problems of breast ultrasound (US) screening. We investigated the added value of deep learning-based computer-aided diagnosis (S-Detect) and shear wave elastography (SWE) to B-mode US for evaluation of breast masses detected by screening US.Between February 2018 and June 2019, B-mode US, S-Detect, and SWE were prospectively obtained for 156 screening US-detected breast masses in 146 women before undergoing US-guided biopsy. S-Detect was applied for the representative B-mode US image, and quantitative elasticity was measured for SWE. Breast Imaging Reporting and Data System final assessment category was assigned for the datasets of B-mode US alone, B-mode US plus S-Detect, and B-mode US plus SWE by 3 radiologists with varied experience in breast imaging. Area under the receiver operator characteristics curve (AUC), sensitivity, and specificity for the 3 datasets were compared using Delong's method and McNemar test.Of 156 masses, 10 (6%) were malignant and 146 (94%) were benign. Compared to B-mode US alone, the addition of S-Detect increased the specificity from 8%-9% to 31%-71% and the AUC from 0.541-0.545 to 0.658-0.803 in all radiologists (All P < .001). The addition of SWE to B-mode US also increased the specificity from 8%-9% to 41%-75% and the AUC from 0.541-0.545 to 0.709-0.823 in all radiologists (All P < .001). There was no significant loss in sensitivity when either S-Detect or SWE were added to B-mode US.Adding S-Detect or SWE to B-mode US improved the specificity and AUC without loss of sensitivity.Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

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