Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study

Chenyang Zhao; Mengsu Xiao; Li Ma; Xinhua Ye; Jing Deng; Ligang Cui; Fajin Guo; Min Wu; Baoming Luo; Qin Chen; Wu Chen; Jun Guo; Qian Li; Qing Zhang; Jianchu Li; Yuxin Jiang; Qingli Zhu

doi:10.3389/fonc.2022.804632

Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study

Front Oncol. 2022 Feb 10:12:804632. doi: 10.3389/fonc.2022.804632. eCollection 2022.

Authors

Chenyang Zhao¹, Mengsu Xiao¹, Li Ma¹, Xinhua Ye², Jing Deng², Ligang Cui³, Fajin Guo⁴, Min Wu⁵, Baoming Luo⁶, Qin Chen⁷, Wu Chen⁸, Jun Guo⁹, Qian Li¹⁰, Qing Zhang¹, Jianchu Li¹, Yuxin Jiang¹, Qingli Zhu¹

Affiliations

¹ Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
² Department of Ultrasound, First Affiliated Hospital, Nanjing Medical University, Nanjing, China.
³ Department of Ultrasound, Peking University Third Hospital, Beijing, China.
⁴ Department of Ultrasound, Beijing Hospital, Beijing, China.
⁵ Department of Ultrasound, Nanjing Drum Tower Hospital, Nanjing, China.
⁶ Department of Ultrasound, Sun Yat-sen Memorial Hospital, Guangzhou, China.
⁷ Department of Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
⁸ Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China.
⁹ Department of Ultrasound, Aero Space Central Hospital, Beijing, China.
¹⁰ Department of Ultrasound, Henan Provincial Cancer Hospital, Zhengzhou, China.

Abstract

Purpose: To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions.

Methods: Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites.

Results: A total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77], p < 0.0001; specificity, S-Detect 74.35% [70.10%-78.28%] vs. US 54.13% [51.42%-60.29%], p < 0.0001), with no decrease in sensitivity. In comparison to that of S-Detect alone, the AUC value significantly was enhanced after combining elastography and S-Detect (0.87 [0.84-0.90]), without compromising specificity (73.93% [68.60%-78.78%]). Significant differences in the S-Detect's performance were also observed across different study sites (AUC of S-Detect in Groups 1-4: 0.89 [0.84-0.93], 0.84 [0.77-0.89], 0.85 [0.76-0.92], 0.75 [0.69-0.80]; p [1 vs. 4] < 0.0001, p [2 vs. 4] = 0.0165, p [3 vs. 4] = 0.0157).

Conclusions: Compared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.

Keywords: breast cancer; computer-aided diagnosis; deep learning; elastography; ultrasound.