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Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China

Authors Zhao C, Xiao M, Jiang Y, Liu H, Wang M, Wang H, Sun Q, Zhu Q

Received 15 October 2018

Accepted for publication 17 December 2018

Published 23 January 2019 Volume 2019:11 Pages 921—930

DOI https://doi.org/10.2147/CMAR.S190966

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 2

Editor who approved publication: Dr Chien-Feng Li


Chenyang Zhao,1,* Mengsu Xiao,1,* Yuxin Jiang,1 He Liu,1 Ming Wang,1 Hongyan Wang,1 Qiang Sun,2 Qingli Zhu1

1Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China; 2Department of Breast Surgery, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China

*These authors contributed equally to this work

Objective: To investigate the feasibility of a CAD system S-detect on a database from a single Chinese medical center.
Materials and methods: An experienced radiologist performed breast US examinations and made assessments of 266 consecutive breast lesions in 227 patients. S-detect classified the lesions automatically in a dichotomous form. An in-training resident who was blind to both the US diagnostic results and histological results reviewed the images afterward. The final histological results were considered as the diagnostic gold standard. The diagnostic performances and interrater agreements were analyzed.
Results: A total of 266 focal breast lesions (161 benign lesions and 105 malignant lesions) were assessed in this study. S-detect had a lower sensitivity (87.07%) and a higher specificity (72.27%) compared with the experienced radiologist (sensitivity 98.1% and specificity 65.43%). The sensitivity and specificity of S-detect were better than that of the resident (sensitivity 82.86% and specificity 68.94%). The AUC value of S-detect (0.807) showed no significant difference with the experienced radiologist (0.817) and was higher than that of the resident (0.758). S-detect had moderate agreement with the experienced radiologist.
Conclusion: In this single-center study, a high level of diagnostic performance of S-detect on 266 breast lesions of Chinese women was observed. S-detect had almost equal diagnostic capacity with an experienced radiologist and performed better than a novice reader. S-detect was also distinguished for its high specificity. These results supported the feasibility of S-detect in aiding the diagnosis of breast lesions on an independent database.

Keywords: ultrasonography, breast neoplasms, image interpretation, computer-assisted, diagnostic imaging


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